Podcasts about mri

We figured out what happened to Nancy Guthrie and it was either a botched wellness check, going through an MRI after swallowing 5 pounds of ball bearings, drowning in the lake after drinking flat soda, or someone kidnapped her to spend Thanksgiving with so they could go viral.   https://www.patreon.com/posts/151358764

A Note from James:In the first episode with Dr. Nicole McNichols, we talked about chemistry, myths, and why communication matters more than performance. This episode goes deeper—into biology, anatomy, dopamine, desire, and the mechanics of pleasure.There are a lot of myths around sex. Some are cultural. Some are Hollywood. Some come from bad science. And some just come from silence.This conversation gets specific. We talk about orgasm, desire, scheduling sex, the so-called “missionary problem,” novelty in long-term relationships, and why so much of what we assume about men and women sexually just isn't true.If Part 1 was about mindset, Part 2 is about understanding how sex actually works.Episode Description:What actually happens in the body during orgasm? Why does anticipation sometimes feel better than the act itself? And why are so many of our beliefs about sex simply wrong?In Part 2 of this three-part series, Dr. Nicole McNichols breaks down the biology of desire, the science of orgasm, and the myths that quietly sabotage long-term relationships.She explains why dopamine peaks during anticipation, why consistency—not intensity—is often key to orgasm, and why “missionary” might be underrated. They explore the anatomy of the clitoris (including research only fully mapped in 2006), the orgasm gap, responsive vs. spontaneous desire, and why scheduling intimacy can actually increase desire.This episode reframes sex not as performance, but as collaboration—an evolving, communicative process rooted in curiosity and growth.What You'll Learn:Why dopamine spikes during anticipation—and how to avoid the post-expectation letdownThe difference between spontaneous and responsive desire (for both men and women)Why consistency is physiologically critical during orgasmThe science behind the orgasm gap and what actually closes itWhy scheduling intimacy can increase frequency and desire—not kill spontaneityTimestamped Chapters:[00:02:00] No One Craves Bad Sex & The Myth of “Boring” Positions[00:03:18] Previously on Part 1: Porn Myths & Feeling Wanted[00:04:00] Chemistry, Pheromones & The Role of Safety[00:06:00] Sexual Growth Mindset & Compatibility[00:08:00] Fireworks vs. Communication[00:10:00] Anatomy, Diversity of Touch & The Clitoris Explained[00:12:00] Scripts, Feedback & How to Talk During Sex[00:17:00] Novelty, Micro-Novelty & Preventing Boredom[00:19:00] Wanting, Liking & Learning: The Pleasure Cycle[00:23:00] Expanding the Definition of Sex[00:25:00] The “Sex Recession” & Frequency Myths[00:27:00] Planning Intimacy & Scheduling Sex[00:31:00] Why Missionary Deserves a Rebrand[00:34:00] Internal Anatomy, the Clitoral Complex & Size Myths[00:39:00] What Is an Orgasm, Physiologically?[00:45:00] The Orgasm Gap & Why Fingering Matters[00:47:00] Consistency vs. “Faster & Harder”[00:49:00] Masturbation Myths & No Nut November[00:51:00] Refractory Period & Aging[00:55:00] Multiple Orgasms & What Research Shows[01:00:00] Love, Orientation & Novelty in Long-Term RelationshipsAdditional Resources:You Could Be Having Better SexNicole McNicholsHelen O'Connell – Research mapping full clitoral anatomy (MRI studies)Beverly Whipple – Orgasm research & physiological studiesA Moveable Feast – Referenced during discussionSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

BONUS: The Future of Seeing—Why AI Vision Will Transform Medicine and Human Perception What if the next leap in AI isn't about thinking, but about seeing? In this episode, Daniel Sodickson—physicist, medical imaging pioneer, and author of "The Future of Seeing"—argues we're on the edge of a vision revolution that will change medicine, technology, and even human perception itself. From Napkin Sketch to Parallel Imaging "I was doodling literally on a napkin in a piano bar in Boston and came up with a way to get multiple lines at once. I ran to my mentor and said, 'Hey, I have this idea, never mind my paper.' And he said, 'Who are you again? Sure, why not.' And it worked."   Daniel's journey into imaging began with a happy accident. While studying why MRI couldn't capture the beating heart fast enough, he realized the fundamental bottleneck: MRI machines scan one line at a time, like old CRT screens. His insight—imaging in parallel to capture multiple lines simultaneously—revolutionized the field. This connection between natural vision (our eyes capture entire scenes at once) and artificial imaging systems set him on a 29-year journey exploring how we can see what was once invisible. Upstream AI: Changing What We Measure "Most often when we envision AI, we think of it as this downstream process. We generate our data, make our image, then let AI loose instead of our brains. To me, that's limited. Why aren't we thinking of tasks that AI can do that no human could ever do?"   Daniel introduces a crucial distinction between "downstream" and "upstream" AI. Downstream AI takes existing images and interprets them—essentially competing with human experts. Upstream AI changes the game entirely by redesigning what data we gather in the first place. If we know a machine learning system will process the output, we can build cheaper, more accessible sensors. Imagine monitoring devices built into beds or chairs that don't produce perfect images but can detect whether you've changed since your last comprehensive scan. AI fills in the gaps using learned context about how bodies and signals behave. The Power of Context and Memory "The world we see is a lie. Two eyes are not nearly enough to figure out exactly where everything is in space. What the brain is doing is using everything it's learned about the world—how light falls on surfaces, how big people are compared to objects—and filling in what's missing."   Our brains don't passively receive images; they actively construct reality using massive amounts of learned context. Daniel argues we can give imaging machines the same superpower. By training AI on temporal patterns—how healthy bodies change over time, what signals precede disease—we create systems with "memory" that can make sophisticated judgments from incomplete data. Today's signal, combined with your history and learned patterns from millions of others, becomes far more informative than any single pristine image could be. From Reactive to Proactive Health "I've started to wonder why we use these amazing MRI machines only once we already know you're sick. Why do we use them reactively rather than proactively?"   This question drove Daniel to leave academia after 29 years and join Function Health, a company focused on proactive imaging and testing to catch disease before it develops. The vision: a GPS for your health. By combining regular blood panels, MRI scans, and wearable data, AI can monitor whether you look like yourself or have changed in worrisome ways. The goal isn't replacing expert diagnosis but creating an early warning system that surfaces problems while they're still easily treatable. Seeing How We See "Sometimes when I'm walking along, everything I'm seeing just fades away. And what I see instead is how I'm seeing. I imagine light bouncing off of things and landing in my eye, this buzz of light zipping around as fast as anything in the universe can go."   After decades studying vision, Daniel experiences the world differently. He finds himself deconstructing his own perception—tracing sight lines, marveling at how we've evolved to turn chaos of sensation into spatially organized information. This meta-awareness extends to his work: every new imaging modality has driven scientific discovery, from telescopes enabling the Copernican Revolution to MRI revealing the living body. We're now at another inflection point where AI doesn't just interpret images but transforms our relationship with perception itself.   In this episode, we refer to An Immense World: How Animal Senses Reveal the Hidden Realms Around Us by Ed Young on animal perception, and A Path Towards Autonomous Machine Intelligence by Yann LeCun on building AI more like the brain.   About Daniel Sodickson Daniel K. Sodickson is a physicist in medicine and chief medical scientist at Function Health. Previously at NYU, and a gold medalist and past president of the International Society for Magnetic Resonance in Medicine, he pioneers AI-driven imaging and is author of The Future of Seeing.

Fluent Fiction - Norwegian: Balancing Hearts: A Radiologist's Valentine's Day Victory Find the full episode transcript, vocabulary words, and more:fluentfiction.com/no/episode/2026-02-19-23-34-02-no Story Transcript:No: På sykehuset var vinterluften kald, men inne i røntgenavdelingen var det en travel atmosfære.En: At the hospital, the winter air was cold, but inside the radiology department, there was a bustling atmosphere.No: Det var Valentine's Day, men det var ingen romantikk i luften her.En: It was Valentine's Day, but there was no romance in the air here.No: I stedet var det forventning.En: Instead, there was anticipation.No: Sigrid, en dedikert radiolog, var spent.En: Sigrid, a dedicated radiologist, was excited.No: Hun hadde gjort research og hadde valgt ut en ny MR-maskin.En: She had done research and had selected a new MRI machine.No: Denne maskinen skulle gjøre arbeidet hennes mer nøyaktig og raskere.En: This machine would make her work more accurate and faster.No: Hun ville ha det beste for pasientene sine.En: She wanted the best for her patients.No: Men Kjell, budsjettansvarlig på sykehuset, var ikke like overbevist.En: But Kjell, the hospital's budget manager, was not as convinced.No: Han var forsiktig med sykehusets penger og ønsket å forsikre seg om at de brukte dem på noe som virkelig var verdt det.En: He was careful with the hospital's money and wanted to ensure that they were spending it on something truly worthwhile.No: På den andre siden av bordet satt Astrid, en energisk representant fra selskapet som leverte medisinsk utstyr.En: On the other side of the table sat Astrid, an energetic representative from the company supplying medical equipment.No: Hun hadde med seg brosjyrer og glimtet i øyet.En: She had brought brochures and had a twinkle in her eye.No: Hun var ivrig etter å avslutte avtalen.En: She was eager to close the deal.No: Rommet der de satt var fylt med kataloger og brosjyrer, og en telefon for å kontakte leverandører.En: The room where they sat was filled with catalogs and brochures, and there was a phone to contact suppliers.No: Sigrid snakket om hvor viktig denne MR-maskinen ville være.En: Sigrid talked about how important this MRI machine would be.No: Hun viste til studier og tall, men Kjell ristet på hodet.En: She referred to studies and numbers, but Kjell shook his head.No: "Er det virkelig nødvendig?", spurte han.En: "Is it really necessary?" he asked.No: Astrid fulgte nøye med.En: Astrid was following closely.No: "Denne maskinen vil forbedre diagnostikken deres betydelig," sa hun og smilte oppmuntrende.En: "This machine will significantly improve your diagnostics," she said with an encouraging smile.No: Hun var god på å presse på, men hun visste også å ikke overselge.En: She was good at pressing the case, but she also knew not to oversell.No: Så kom øyeblikket som endret alt.En: Then came the moment that changed everything.No: Sigrid trakk frem en fersk kasustikk.En: Sigrid brought out a recent case study.No: Det handlet om en pasient som fikk diagnosen sin sent fordi sykehuset fortsatt brukte gammelt utstyr.En: It was about a patient who received a late diagnosis because the hospital still used old equipment.No: Alle var stille mens hun snakket.En: Everyone was silent as she spoke.No: Til og med Kjell krummet rygg.En: Even Kjell slumped slightly.No: Etter en pause lente Kjell seg fremover.En: After a pause, Kjell leaned forward.No: "Det er klart pasientsikkerhet er viktigst," sa han.En: "It's clear that patient safety is the most important," he said.No: "Vi skal finne midler til å kjøpe denne maskinen."En: "We'll find the funds to buy this machine."No: Astrid smilte bredt.En: Astrid smiled broadly.No: "Jeg kan gi dere en spesiell rabatt," tilbød hun.En: "I can offer you a special discount," she suggested.No: Det var en løsning som tilfredsstilte alle og brakte maskinen innenfor budsjettet.En: It was a solution that satisfied everyone and brought the machine within budget.No: Sigrid følte en lettelse.En: Sigrid felt relieved.No: Hun hadde lært viktigheten av å balansere lidenskap med praktiske realiteter.En: She had learned the importance of balancing passion with practical realities.No: Kjell hadde også fått innsikt i at noen ganger kunne innovative løsninger være et klokt valg for både budsjett og omsorg.En: Kjell had also gained insight that sometimes innovative solutions could be a wise choice for both budgeting and care.No: Til slutt, mens snøen dalte utenfor, visste de alle at de sammen hadde tatt en beslutning som ville være til det beste for alle pasientene som ville komme inn gjennom sykehusdørene.En: In the end, as the snow fell outside, they all knew that together they had made a decision that would be best for all the patients who would come through the hospital doors.No: Det var en seier for både medisin og ansvarlig økonomisk styring.En: It was a victory for both medicine and responsible financial management. Vocabulary Words:radiology: røntgenavdelingenbustling: travelanticipation: forventningdedicated: dedikertaccurate: nøyaktigbudget: budsjettconvinced: overbevistrepresentative: representantbrochures: brosjyrerdiagnostics: diagnostikkenencouraging: oppmuntrendeoversell: overselgeinsight: innsiktpatient safety: pasientsikkerhetdiscount: rabattrelieved: lettelsepractical: praktiskebalancing: balansereinnovative: innovativediagnosis: diagnosensuppliers: leverandørertwinkle: glimtetcatalogs: katalogerstudies: studiernumbers: tallencouraging: oppmuntrendecase study: kasustikkslumped: krummet ryggsolution: løsningfinancial management: økonomisk styring

Most symptoms we're dealing with as active women and runners aren't random at all, but are instead clues our body is trying to give us.In this episode, Louise, a multi-award-winning women's integrative health practitioner, shares three powerful lessons from her own health journey and client experiences that every woman should know, especially during perimenopause. She explains how hormone shifts can impact everything from medical testing accuracy to inflammation, fat loss resistance, stress sensitivity, and even unexpected symptoms like canker sores. One key insight is the importance of timing medical screenings, such as breast MRIs, with your menstrual cycle to improve accuracy. Another is the complex relationship between estrogen, histamine, and hormone replacement therapy, and how individual genetics can influence reactions.Louise also highlights how stress, calorie restriction, and hormonal fluctuations can trigger symptoms that seem unrelated, and offers practical strategies to manage acute issues while still addressing root causes like gut health and hormone balance.This episode is a reminder that women's health is highly individualized. Understanding your unique physiology, tracking patterns, and using targeted strategies can help you feel more in control, improve performance, and support long-term health through every stage of life.Explore my "All About Gut Health" and other masterclasses here: https://www.breakingthroughwellness.com/store Link to my FullScript where you can see curated best supplement picks and save 20%: https://us.fullscript.com/welcome/breakingthroughwellness/store-start Learn and level up with my free nutrition guide and award-winning Badass Breakthrough Academy to thrive through perimenopause with less stress: https://www.breakingthroughwellness.com/Take advantage of our podcast listener discount and save 20% off all of Kion's science-backed clean products. Code "LOUISE" saves on all future orders: : https://www.getkion.com/pages/maximizing Episode Highlights:(0:00) Intro(3:05) Three important women's health lessons(6:15) Missed eligibility for early breast screening(9:46) Hormones affecting MRI and mammogram accuracy(13:09) Best timing for breast imaging in cycle(15:10) Hormone optimization and HRT considerations(19:01) Estrogen patch reaction case study(21:58) Estrogen, histamine, and mast cell connection(27:14) Managing histamine overload and recovery(35:24) Stress sensitivity during perimenopause explained(40:00) Hormones, stress, and canker sore triggers(42:49) Gut health and autoimmune connections(46:39) L-lysine for acute outbreak management(51:08) Symptom relief vs root cause approach(52:19) Upcoming resources and listener updates(54:15) OutroTune in weekly to "Maximizing Hormones, Physique, and Running Through Perimenopause" for our simple female-specific science-based revolution. Let's unlock our best with less stress!I'd love to connect! Email

Every week there’s a new study telling us what not to eat. Coffee is bad. Eggs are dangerous. Spinach blocks nutrients. Or so we’re told. Add in cholesterol numbers, preventative scans, detox trends and a constant stream of “toxic” food warnings online, and it’s easy to start second-guessing what’s on your plate. Eating was never meant to feel this stressful. So I sat down with Dr Joanna McMillan - nutrition scientist, dietitian, author of The Fibre Factor, and one of Australia’s most trusted voices in evidence-based nutrition to cut through the noise. Joanna has spent decades translating complex research into practical advice, and she brings much-needed sanity to the way we think about food and health. If you’ve ever panicked over a blood test result, felt unsure about whether to book another scan, or wondered who to trust when it comes to nutrition advice, this conversation will steady you. Joanna and I discuss: The risk of becoming part of the “worried well” and over-testing your health The big ticket preventative checks Joanna prioritises at milestone ages What a coronary calcium score is and when it might be useful Why full body MRI scans may not be the smartest health investment The biggest nutrition myths circulating online, including anti-plant rhetoric Joanna’s core eating philosophy as a plant-rich omnivore Why diversity of fibre matters more than just soluble vs insoluble What actually happens in your gut when you suddenly double your fibre intake The supplements Joanna personally takes and how to assess supplement quality Why joy at mealtimes might be one of the most underrated health habits Key quotes “There is a risk of overdoing it. We talk about the worried well, and sometimes you can become so worried about your health, you forget about celebrating the things that are good.” “Your body, given the right tools, does detox beautifully all by itself.” Connect with Dr Joanna McMillan on Instagram, LinkedIn, and her website, and check out her latest book The Fibre Factor. My latest book The Health Habit is out now. You can order a copy here: https://www.amantha.com/the-health-habit/ Connect with me on the socials: Linkedin (https://www.linkedin.com/in/amanthaimber) Instagram (https://www.instagram.com/amanthai) If you are looking for more tips to improve the way you work and live, I write a weekly newsletter where I share practical and simple to apply tips to improve your life. You can sign up for that at https://amantha-imber.ck.page/subscribe Visit https://www.amantha.com/podcast for full show notes from all episodes. Get in touch at amantha@inventium.com.au Credits: Host: Amantha Imber Sound Engineer: The Podcast Butler See omnystudio.com/listener for privacy information.

Most people think of a knee injury as a knee problem. You tear something, you rehab it, you move on. But the science tells a very different story — one where a single traumatic injury quietly drives cartilage degradation, cardiovascular impairment, and systemic inflammation for decades after the initial damage has "healed." I got a firsthand look at this when an MRI revealed two meniscus tears, a split MCL, and early-onset osteoarthritis in my left knee. That last one was humbling. I always assumed osteoarthritis happened to other people — older, less active people. Not someone who squats heavy and trains consistently. In this episode, Forrest Smith — CEO and Co-founder of Kineon Labs, a health technology company specializing in targeted red light and laser therapy devices — returns for his third appearance on the podcast. And the picture he paints of what happens inside an injured joint long after the rehab is over is sobering. For example, the NFL tracked over 3,500 players who'd returned to competition after knee injuries and found chronic inflammation still present 10 to 20 years later, despite world-class rehab.  Notably, the quads on the players' injured side ran one to two degrees colder, a sign of impaired cardiovascular delivery. And the risk of major cardiovascular events jumped by 50% – not because of the original injury, but because of inflammation that never resolved. That's the cycle most people don't know they're stuck in. And it's where laser-based photobiomodulation changes the equation. Targeted 808nm lasers can drop inflammatory markers like TNF-alpha and IL-6 by 70 to 85% within days. Once that chronic degradation slows down, chondroblasts — the fast-growing front end of cartilage — can actually proliferate and begin rebuilding the extracellular matrix. Slow the destruction on one side, accelerate the biology on the other. That's what "regrowing cartilage" actually means. Penetration depth is what makes lasers fundamentally different from LEDs. At five to seven centimeters of reach, you're dosing 10 to 100 times more tissue volume than a surface-level panel can touch. Then there's the other side of this that almost nobody talks about: the ibuprofen your doctor hands you after surgery. Research shows that 90 days of use increases heart attack risk by 48%, heart failure by 35%, and major coronary events by 75% — while actively impairing the collagen and fibroblast function your body needs to heal. It's doing the exact opposite of what most people assume. If you've ever dealt with a joint injury, chronic inflammation, or just assumed over-the-counter painkillers were harmless, this one's worth your time. About Forrest Smith: Co-Founder and CEO of Kineon, a health-tech leader who spent 18 years in China building hardware startups and mastering the local supply chain. A lifelong athlete and CrossFit enthusiast, he founded Kineon after developing a portable, medical-grade laser device to treat his own chronic knee pain.  Website: https://kineon.io/blogs/authors/forrest-smith  [Discount Code] Use code MKUMMERMOVE for 10% off the Kineon Move+ Pro:  https://michaelkummer.com/go/kineon Learn more: Kineon Move+ Pro Review: https://michaelkummer.com/kineon-move-plus-review/ Benefits of Red Light Therapy for Joint Pain and Arthritis: https://michaelkummer.com/red-light-therapy-for-joints/  Thank you to this episode's sponsor, Peluva! Peluva makes minimalist shoes to support optimal foot, back and joint health. I started wearing Peluvas several months ago, and I haven't worn regular shoes since. I encourage you to consider trading your sneakers or training shoes for a pair of Peluvas, and then watch the health of your feet and lower back improve while reducing your risk of injury.  To learn more about why I love Peluva barefoot shoes, check out my in-depth review: https://michaelkummer.com/health/peluva-review/  And use code MICHAEL to get 10% off your first pair: https://michaelkummer.com/go/peluva  In this episode: 00:00 Intro 00:42 Mk's knee MRI (meniscus, MCL, osteoarthritis) 03:42 Traumatic knee damage, synovial capsule & acute vs chronic inflammation 06:42 Can you regrow cartilage?  08:11 Hidden systemic effects: Cardiovascular impairment from chronic joint inflammation 09:50 Post-surgery recovery + the NSAID dilemma  12:28 NSAIDs: Cardiovascular risk & slower tissue repair  16:36 Kineon Move+ Pro knee protocol 17:59 Placement tips 20:36 Penetration depth 21:41 Hamstring strain case study  26:55 The future: Brain & gut photobiomodulation  33:20 Final thoughts Find me on social media for more health and wellness content: Website: https://michaelkummer.com/ YouTube: https://www.youtube.com/@MichaelKummer Instagram: https://www.instagram.com/primalshiftpodcast/ Pinterest: https://www.pinterest.com/michaelkummer/ Twitter/X: https://twitter.com/mkummer82 Facebook: https://www.facebook.com/realmichaelkummer/ [Medical Disclaimer] The information shared on this video is for educational purposes only, is not a substitute for the advice of medical doctors or registered dietitians (which I am not) and should not be used to prevent, diagnose, or treat any condition. Consult with a physician before starting a fitness regimen, adding supplements to your diet, or making other changes that may affect your medications, treatment plan, or overall health. [Affiliate Disclaimer] I earn affiliate commissions from some of the brands and products I review on this channel. While that doesn't change my editorial integrity, it helps make this channel happen. If you'd like to support me, please use my affiliate links or discount code. #Kineon #RedLightTherapy     

Bonus content you didn’t hear on the show! We dive into the oddly fascinating ways our bodies aren’t symmetrical, and we unpack even more details about Kincaid’s Goodwill dilemma. He also shares a truly frightening experience he once had during an MRI. Plus, Dallas reveals the surprising reason she still uses a very specific type of knife. All that and more in this behind‑the‑scenes episode!See omnystudio.com/listener for privacy information.

Imaging biomarkers over invasive biopsies! Minal Jagtiani MD, and Suraj Serai, PhD, speak with host, Raisa Amiruddin, MBBS, on safe repeatable tracking of pediatric liver iron, fat, and fibrosis with quantitative MRI. Learn the physics that makes the liver look bright and techniques to keep the measurements precise. 

You can't delegate your longevity to a system that only gets paid when you're sick. In this episode of The Game Changing Attorney Podcast, Michael Mogill sits down with Dr. Bill Kapp, Chief Medical Officer and co-founder of Fountain Life, to explore the cutting edge of longevity science. Dr. Kapp reveals how creating a comprehensive digital twin with 250 gigabytes of personalized health data can detect fatal conditions 20 to 30 years before symptoms appear, why your family doctor is 17 to 20 years behind the latest technology, and how exponential innovations from gene editing to AI-powered diagnostics are reshaping what's possible for extending your healthspan. This conversation cuts through the influencer noise in the longevity space to focus on data-driven approaches backed by science, not hype. Here's what you'll learn: How full-body MRI scans with 10,000 slices and whole genome sequencing create a complete digital twin that enables personalized optimization Why muscle mass is the number one predictor of disease-free longevity and how lifting heavy outweighs everything else you can do Why you need to become the CEO of your own health and stop delegating your longevity to a broken medical system What you don't measure, you can't manage. It's time to become the CEO of your own health. ---- Show Notes: 02:39 – What Fountain Life is and the paradigm shift from symptom-based to proactive care. 12:13 – The comprehensive assessment: what gets measured and why it matters. 16:48 – The real risk of waiting and the airplane maintenance analogy. 20:01 – Genetics versus lifestyle: what's actually in your control. 26:01 – Making longevity technology accessible and what's coming next. 30:34 – Beyond detection: optimizing cellular health, hormones, and mitochondrial function. 41:29 – Longevity escape velocity and whether we can reverse aging in our lifetime. 44:06 – High-performance aging: why 80 doesn't have to mean slowing down. 45:45 – The top 3 takeaways: baseline testing, sleep optimization, and lifting heavy. ---- Links & Resources: Fountain Life Dr. Bill Kapp Tony Robbins Dr. Peter Diamandis Dr. Bob Hariri Why We Sleep by Dr. Matthew Walker ---- Do you love this podcast and want to see more game changing content? Subscribe to our YouTube channel. ---- Past guests on The Game Changing Attorney Podcast include David Goggins, John Morgan, Alex Hormozi, Randi McGinn, Kim Scott, Chris Voss, Kevin O'Leary, Laura Wasser, John Maxwell, Mark Lanier, Robert Greene, and many more. ---- If you enjoyed this episode, you may also like: 396. Why High Performers Can't Afford to Ignore Wellness with Dr. Taz Bhatia 283. Marcus Filly — Fitness Secrets for Professional Success 41. Dave Asprey —Becoming Bulletproof: Living Your Longest and Healthiest Life

In this episode, we discuss the Netherlands' proposed 36% tax on unrealized capital gains, unpacking what it means to tax wealth that exists only on paper and how such a policy could force asset sales, distort investment behavior, and reshape long-term incentives for savers and entrepreneurs. For our Foolishness of the Week, we turn to North Carolina, where a local official distinguished himself as perhaps the dumbest sheriff in America. We then welcome Dave Greene for an extended conversation on health insurance, exploring how risk pooling actually works, why medical pricing feels arbitrary, how regulation and the Affordable Care Act altered incentives for insurers and patients, and why price opacity and third-party payment continue to drive costs higher across the system. 00:00 Introduction and Overview 00:31 Words and Numbers Backstage & Listener Shoutouts 04:13 The Netherlands' 36% Tax on Unrealized Gains 08:20 Who Can Afford Risk Under a Wealth-Style Tax? 12:24 Florida Snow & Strange Weather 13:39 Foolishness of the Week: The Mecklenburg Sheriff 18:54 Dave Greene Introduction: Health Insurance Insider Perspective 21:36 Why Health Insurance Feels So Frustrating 24:05 Is the System Designed to Make You Give Up? 27:32 Why Health Care Prices Stay Hidden 34:13 The $1,600 MRI vs. $200 MRI Problem 41:38 Negotiating Medical Bills (Yes, You Can) 43:36 The Affordable Care Act and Incentive Distortions 47:24 Health Insurance Profit Margins Explained 50:45 1950s Health Care vs. Today's Innovation 53:48 Why Insurance Companies Get the Blame 57:26 Medicare vs. Private Insurance Subsidies 01:01:35 Guest Outro and Closing Thoughts Learn more about your ad choices. Visit podcastchoices.com/adchoices

Thanks to our Partners, NAPA Auto Care and NAPA TRACS Watch Full Video Episode "The reality of a client advocates daily work is translating fear into clarity." Shop owner and coach Clint White explores a powerful shift at the auto repair front counter, from “Service Advisor” to “Repairathist.” He explains that because vehicles represent freedom and control, many customers arrive feeling anxious and financially defensive. As a result, the Repairathist's role becomes part technician, part therapist, focused on translating fear into clarity and helping people feel understood. Customers aren't buying parts, he says; they're buying relief. The conversation dives into how to put this mindset into practice, starting with a “language shift” that replaces industry jargon like “diag” and “DVI” with clear, value-based explanations. This approach invites customers into the process instead of making them feel excluded. White also stresses the importance of transparency, showing clients the “MRI and X-ray” of their vehicle before prioritizing repairs, and ensuring that front counter promises align with what happens in the shop. Ultimately, the episode defines the Repairathist as a professional with an “others first” mindset who builds trust through empathy, honesty, and consistency—delivering an experience so positive that customers remember how they felt more than what they spent. Timestamps 00:00:00 – Introduction 00:01:45 – Introducing the "Repairist" 00:03:15 – Therapy at the Counter: Clint explains that a Client Advocate's role is akin to a therapist, tasked with "translating fear into clarity" for anxious customers. 00:06:45 – The Psychology of the Car: Discussion on how vehicles represent freedom and control, making repairs an emotional issue rather than just a mechanical one. 00:10:15 – Selling Relief, Not Parts: Clint delivers the key insight that customers are not buying repairs; they are buying "relief from their current situation". 00:11:30 – The Experience Economy: The "Steak Dinner" analogy—customers don't remember the price as much as they remember how the experience made them feel. 00:14:00 – The Language Shift: Clint warns against using jargon like "diag" or "DVI," which makes customers feel excluded or stupid. He suggests using "testing and procedures" instead. 00:19:15 – Transparency & The MRI: Clint advocates for showing the customer "everything that is knowable" (the MRI/X-ray) before asking them to make a decision. 00:20:45 – Hiring for Heart: Clint explains that he hires for a "servant's heart" first; technical knowledge is secondary to empathy. 00:22:00 – The ROI of Empathy: Discussion on the business benefits of this mindset, including "sticky" clients, reduced staff turnover, and better reputation. 00:26:45 – Relationship vs. Transaction: Clint defines success not by money, but by building relationships strong enough that clients send Christmas cards years later Clint White, Coaching with Integrity, clint@coachingwithintegrity.llc Thanks to our Partners, NAPA Auto Care and NAPA TRACS Learn more about NAPA Auto Care and the benefits of being part of the NAPA family by visiting https://www.napaonline.com/en/auto-care NAPA TRACS will move your shop into the SMS fast lane with onsite training and...

Dr. Reni Butler speaks with Dr. Anne Marie McCarthy and Dr. Christine Edmonds about their study examining Black–White racial differences in background parenchymal enhancement (BPE) on contrast-enhanced breast MRI. They discuss the finding that black women had higher odds of high BPE independent of breast density, explore potential biologic and environmental drivers, and consider how quantitative BPE assessment could improve breast cancer risk stratification and screening equity. Black-White Racial Differences in Background Parenchymal Enhancement at Breast MRI. Mahmoud et al. Radiology 2026; 318(1):e251041.

PSA spikes, abnormal MRI results, and high PI-RADS scores often trigger immediate fear and for many men, that fear leads straight to biopsy. In this episode, Dr. Stephen Petteruti breaks down what PSA actually measures, how MRI technology fits into modern prostate cancer management, and why a high PI-RADS score does not automatically equal aggressive disease. Dr. Stephen discusses active surveillance, non-biopsy monitoring strategies, cardiovascular risk, hormone balance, and why overtreatment may compromise quality of life more than the cancer itself.  For men who value proactive healthcare, evidence-based medicine, testosterone preservation, and long-term vitality, this conversation offers clarity in a space dominated by urgency and assumption. It reframes prostate cancer care around informed consent, individualized risk assessment, and protecting both lifespan and healthspan.Before agreeing to your next scan or biopsy, press pause. Listen carefully. Ask better questions. Watch the episode of Stop the Prostate Biopsy Frenzy: The Truth About MRI, PI-RADS, and PSA.Enjoy the podcast? Subscribe and leave a 5-star review on your favorite platforms.Dr. Stephen Petteruti is a leading Functional Medicine Physician dedicated to enhancing vitality by addressing health at a cellular level. Combining the best of conventional medicine with advancements in cellular biology, he offers a patient-centered approach through his practice, Intellectual Medicine 120. A seasoned speaker and educator, he has lectured at prestigious conferences like A4M and ACAM, sharing his expertise on anti-aging. His innovative methods include concierge medicine and non-invasive anti-aging treatments, empowering patients to live longer, healthier lives.Website: https://www.drstephenpetteruti.com/ Practice: www.intellectualmedicine.com YouTube: https://www.youtube.com/@intellectualmedicine LinkedIn: https://www.linkedin.com/in/drstephenpetteruti/ Instagram: https://www.instagram.com/dr.stephenpetteruti/ Facebook: https://www.facebook.com/dr.stephenpetteruti    Disclaimer:  The content presented in this video reflects the opinions and clinical experience of Dr. Stephen Petteruti and is intended for informational and educational purposes only. It is not medical advice and should not be used as a substitute for professional diagnosis, treatment, or guidance from your personal healthcare provider. Always consult your physician or qualified healthcare professional before making any changes to your health regimen or treatment plan.Produced by https://www.BroadcastYourAuthority.com 

A stiff back and aching leg can look like a simple lower spine problem until the real culprit hides higher up. We share Diane's journey from conservative care to targeted injections and, finally, two precisely timed surgeries that unlocked lasting relief. Along the way, our interventional pain specialist and neurosurgeon walk through how they think, starting with imaging and exam, testing hypotheses with blocks and epidurals and only then decide whether surgery is the safest, most effective move.You'll hear how short-lived relief from medial branch blocks, rhizotomy, and epidural injections became a diagnostic signal, not a failure. The turning point came with a second MRI that revealed severe cervical stenosis compressing the spinal cord, an insight that explained leg symptoms and shifted priority to neck surgery first. We break down why the cervical spine often takes precedence, how operating where the danger is highest can also calm symptoms elsewhere and why “smallest effective surgery” often beats a bigger, hardware-heavy approach.For more content from Centra Health check us out on the following channels.YouTubeFacebookInstagramTwitter

On today's Good Day Health Show - ON DEMAND…Host Doug Stephan and Dr. Ken Kronhaus of Lake Cardiology (352-735-1400) cover a number of topics affecting our health. First up, Doug and Dr. Ken begin with the brain, specifically how it can be enhanced and how it can be damaged. There's a new study about a silent brain disease, Amyloid protein buildup in the brain being a hallmark of Alzheimer's disease and other neurodegenerative conditions, and a massive review on how to best help your brain through depression. Moving on to AI diagnostics, the latest in medical technology involves an AI system capable of interpreting MRIs in seconds, flagging strokes or hemorrhages, and drastically cutting down the time to treatment in ER settings.Then, a focus on men's cardiovascular health showing an increase in cardiovascular disease risk, starting at age 35, much earlier than women, suggesting the preventative screening needs to begin by mid-30s. Lastly, a recent scientific review as provided reassuring data for pregnant women that there is no increased-risk of autism , ADHD, or any intellectual disability in children. It's important to remember to follow dosage guidelines when it comes to acetaminophen (Tylenol).  Website: GoodDayHealthShow.com Social Media: @GoodDayNetworks

Opereren met een hologram voor je ogen. In het Amsterdam UMC werd onlangs een hersenoperatie uitgevoerd terwijl de neurochirurg door een Augmented Reality-bril keek. Het is een technologische ontwikkeling die in meerdere Nederlandse ziekenhuizen in volle gang is. In deze aflevering van BNR Beter bespreekt presentator Nina van den Dungen met twee experts wat Virtual Reality (VR) en Augmented Reality (AR) nu écht betekenen voor de chirurgie, en of dit de operatiekamer fundamenteel gaat veranderen. Te gast zijn: Maarten Bot – neurochirurg bij het Amsterdam UMC, die met een Microsoft HoloLens een drain in de hersenen plaatste op basis van een 3D-hologram. Lideke van der Steeg – kinderchirurg bij het Prinses Máxima Centrum en leider van de onderzoeksgroep die AR inzet om operaties bij kinderen nauwkeuriger te maken, onder meer met de Apple Vision Pro. Bot gebruikt AR bij (spoed)operaties in de neurochirurgie. Op basis van een MRI of CT-scan projecteert hij een hologram van de hersenen direct op het hoofd van de patiënt. Zo ziet hij tijdens het opereren precies waar structuren in de diepte liggen, met als doel het aantal misplaatsingen van drains, nu nog zo’n 20 procent, verder terug te dringen. Van der Steeg experimenteert in Utrecht met AR bij kinderen met ribtumoren. Door een 3D-model van de borstkas op de patiënt te projecteren, kan ze mogelijk een extra kijkoperatie overslaan. Minder ingrepen betekent minder complicaties en minder belasting voor het kind. In de uitzending hoor je ook een reportage uit de operatiekamer van het Wilhelmina Kinderziekenhuis, waar verslaggever Stijn Goossens zelf een demo krijgt van promovendus en technisch geneeskundige Nick de Groot en postdoctoraal onderzoeker Matthijs Fitski, die als levend proefpersoon meewerkt in het experiment. Nick en Matthijs werken bij het Prinses Máxima Centrum aan de ontwikkeling van de 3D-technologie. Stijn ervaart wat je ziet met de Apple Vision Pro op en waar de technologie nog verder in moet doorontwikkelen. Vragen of opmerkingen over deze aflevering? Mail de redactie: Stijn GoossensSee omnystudio.com/listener for privacy information.

✅ Watch the MASTERCLASS on Low Back Pain & Sciatica.https://visit.shapeshiftwellness.com/bbp-masterclass-5Why do chronic pain flare-ups last so long? And what do you do when it feels like everything makes your back pain worse?In this member Q&A, Dr. Anthony Davis and Dr. Blake answer real questions from people dealing with chronic low back pain, sciatica, nerve pain, disc bulges, and fear of movement.If you've ever felt stuck in the flare-up rollercoaster, this episode is for you..Here's everything we cover:• Why do flare-ups last 3–4 weeks? Is that normal?• If pain is stress-related, why doesn't it calm down as quickly as it started?• How do you “switch off” the brain's pain response?• What if sitting hurts… but standing hurts too?• How do I prove to myself I'm not damaged when I hurt every day?• Meditation isn't helping my pain — now what?• Am I regressing… or is this just part of recovery?• Is sacral or glute pain actually coming from my back?• How do I overcome the fear of damaging my back with movement?• My hips pop during exercises — am I out of alignment?• If something clicks but doesn't hurt, should I worry?• Should I adjust pelvic tilt or hip rotation during exercises?• What do you think about swimming for back pain rehab?• How do I test new exercises without triggering a flare-up?• How do I protect my shoulders during upper body training?• Does my MRI diagnosis (stenosis, spondylolisthesis, disc issues) actually matter?.0:00 When Everything Makes Pain Worse8:42 Fear, Catastrophizing & Feeling “Worse”25:24 Why Flare-Ups Last 3–4 Weeks33:20 MRI Results & Diagnosis Myths35:44 Protecting Your Shoulders37:10 Hip Popping & Alignment Fears40:30 Swimming & Testing New Exercises47:40 Relief Positions vs Real Rehab.#lowbackpain #lowbackpainrelief#lowbackpainexercises #discherniation #sciaticarelief#sciatica #sciaticatreatment..⚠️ THIS IS NOT MEDICAL ADVICE! CONSULT YOUR PHYSICIAN BEFORE ENGAGING IN EXERCISE. Do not attempt to self-diagnose or treat. If you engage in this exercise or exercise program, you agree that you do so at your own risk, are voluntarily participating in these activities, assume all risk of injury to yourself. This content is purely for educational purposes.

When does a metatarsal stress reaction actually show up on imaging? In this episode of the Doc On The Run Podcast, Dr. Christopher Segler explains the difference between a stress response, stress reaction, and true stress fracture—and why timing matters when choosing X-rays, MRI, ultrasound, or CT scans. Learn how early imaging can help you make smarter race decisions, avoid false reassurance from a “normal” X-ray, and protect your fitness without turning a minor stress reaction into a full fracture.

Get My Brand Master list⁠: https://drchristiangonzalez.com/best-brands-form-2-2/ Get Liver Supplement Guide: https://drchristiangonzalez.com/liver-supplements-pdf-request-form/ → My one stop shop for quality supplements: https://theswellscore.com/pages/drg Episode Description Over 100 million Americans have some form of liver disease right now—and most don't know it. Your liver doesn't hurt when it's inflamed. No pain, no warning signs, not until it's too late. Meanwhile, the supplement industry is flooding the market with "liver support" formulas packed with proprietary blends, underdosed ingredients, and zero clinical evidence. Dr. Christian Gonzalez went through all the research to find the five best evidence-based liver supplements—proven in human trials to actually protect and repair your liver. In this episode, Dr. G reveals: • The omega-3 dosage shown to reduce liver fat on MRI imaging • Which vitamin E study in the New England Journal of Medicine showed reversal of liver damage • The supplement that activates your body's "master metabolic switch" for fat burning • Why milk thistle has been the gold standard for liver health for over 2,000 years He's ranking each supplement by strength of clinical evidence, giving you exact dosages, who should take them, who shouldn't, and his top brand picks. If you drink alcohol, take medications regularly, eat processed foods, or just live in the modern world—your liver needs support. This episode shows you how. Timestamps: 0:00 - Intro 1:47 - How to Know If Your Liver Is Inflamed 2:54 - The Turmeric Mistake Most People Make 5:34 - The Fatty Acid That Burns Liver Fat 7:43 - The Vitamin E Study That Changed Everything 9:29 - The Blood Sugar Supplement Going Viral 11:54 - Two Supplements Your Doctor Should Know About 13:21 - The 2,000-Year-Old Gold Standard Learn more about your ad choices. Visit megaphone.fm/adchoices

Send a textIn this segment, Ben and Daphna review a retrospective study from the Hospital for Sick Children comparing outcomes of therapeutic hypothermia in late preterm (34-35 weeks) versus early term (36-37 weeks) infants. They discuss the significantly higher rates of mortality, hemodynamic instability, and hypoglycemia found in the younger cohort, known as "Group 1". The hosts explore the implications of using MRI scoring systems like the Weeke score for preterm brains and debate the ethical challenges of conducting future randomized trials as clinical practice shifts away from cooling younger babies based on emerging retrospective data.----Whole-body hypothermia in late preterm and early term infants: a retrospective analysis from a neurocritical care unit. Martinez A, Cikman G, Al Kalaf H, Wilson D, Banh B, Abdelmageed W, Beamonte Arango I, Christensen R, Branson HM, Cizmeci MN.Pediatr Res. 2026 Jan 7. doi: 10.1038/s41390-025-04701-x. Online ahead of print.PMID: 41501407Support the showAs always, feel free to send us questions, comments, or suggestions to our email: nicupodcast@gmail.com. You can also contact the show through Instagram or Twitter, @nicupodcast. Or contact Ben and Daphna directly via their Twitter profiles: @drnicu and @doctordaphnamd. The papers discussed in today's episode are listed and timestamped on the webpage linked below. Enjoy!

Broadcast from KSQD on 5-30-2024 and replayed on 2-12-2026: Cognitive errors in medicine dismissing unusual presentations as psychological. A case of Pediatric Autoimmune Neuropsychiatric disorders Associated with Streptococcal Infections (PANDAS). Anti-NMDA receptor encephalitis causing psychiatric symptoms. Failures of genetic research to identify causes. The Need for integrating neurology and psychiatry; Importance of testing for antibodies and using MRI scans. Detailed explanation of immune tolerance, peripheral tolerance, and the phenomenon of molecular mimicry in diseases like multiple sclerosis and celiac disease. Importance of addressing root causes rather than just symptoms. Historical context and current advancements in treating autoimmune diseases like type 1 diabetes, lupus, and multiple sclerosis using reprogrammed immune cells and iron oxide nanoparticles. Explanation of how the liver filters blood and helps establish immune tolerance by processing cellular debris and antigens. Advances in engineering regulatory T cells to target specific disease sites and calm inflammatory responses. Exploration of new diagnostic tools and the potential of AI in understanding complex psychiatric conditions.

Murphy has a little help getting through his MRI. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

The worst pain is unexplained pain. In this episode of the Hands-On, Hands-Off Podcast, physical therapists Amy McDevitt and Paul Mintkin explore why pain without a clear diagnosis is often the most distressing—and how physical therapists can communicate pain more effectively when imaging, MRI findings, and pathoanatomy don't provide clear answers.This conversation dives deep into pain science, musculoskeletal pain, low back pain, and the limitations of medical imaging in explaining symptoms. We discuss how over-reliance on MRI results can increase fear, catastrophizing, and confusion for patients—and how language, context, and functional diagnosis can dramatically change outcomes.Learn how to reframe pain using the ICF model, why pain does not equal tissue damage, and how PTs can shift from chasing a pain generator to treating the whole person. The episode includes a real-time patient role-play, practical communication strategies, and insights on direct access physical therapy, lifestyle factors (sleep, stress, activity), and the future of PT education.This episode is essential listening for physical therapists, manual therapists, rehab professionals, and students looking to improve patient communication, reduce fear, and deliver truly person-centered care.

Host Dr. Davide Soldato and guests Dr. David Einstein and Dr. Ravi Madan discuss JCO article, "National Cancer Institute's Working Group on Biochemically Recurrent Prostate Cancer: Clinical Trial Design Considerations," underscoring the need for a consensus on clinical trial designs implementing novel endpoints in this population, the importance of PSA doubling time as a prognostic factor and with an emphasis on treatment de-escalation to limit toxicity and improve patient outcomes. TRANSCRIPT The disclosures for guests on this podcast can be found in the show notes. Davide Soldato: Hello and welcome to JCO After Hours, the podcast where we sit down with authors from some of the latest articles published in the Journal of Clinical Oncology. I am your host, Dr. Davide Soldato, medical oncologist at Ospedale San Martino in Genoa, Italy. Today, we are joined by JCO authors Dr. David Einstein and Dr. Ravi Madan. Dr. Einstein is a medical oncologist specializing in genitourinary malignancy working at Beth Israel Deaconess Medical Center, part of the DFCI Cancer Center, and an assistant professor at Harvard Medical School. Dr. Madan is a senior clinician at the National Cancer Institute (NCI), where he focuses on conducting clinical research in prostate cancer, particularly in the field of immunotherapy. Today, we will be discussing the article titled, "National Cancer Institute's Working Group on Biochemically Recurrent Prostate Cancer: Clinical Trial Design Considerations." So, thank you for speaking with us, Dr. Einstein and Dr. Madan. David Einstein: Thanks for having us. This is a great pleasure. Ravi Madan: Appreciate being here. Davide Soldato: So, I just want to start from a very wide angle. And the main question is why did you feel that there was the need to convey a consensus and a working group to talk about this specific topic: biochemically recurrent prostate cancer? What has been the change in current clinical practice and in the trial design that we are seeing nowadays? And so, why was it necessary to convey such a consensus and provide considerations on novel clinical trials? David Einstein: Yeah, so I think it's very interesting, this disease state of biochemically recurrent prostate cancer. It's very different from other disease states in prostate cancer, and we felt that there was a real need to define those differences in clinical trials. Years ago, metastatic castration-resistant prostate cancer was the primary disease state that was explored, and over time, a lot of things shifted earlier to metastatic disease defined on a CAT scan and bone scan to an earlier disease state of metastatic castration-sensitive prostate cancer. And the clinical trial principles from late-stage could be applied to MCSPC as well. However, BCR is very different because the patients are very different. And for those reasons, there are unique considerations, especially in terms of toxicity and treatment intensity, that should be applied to biochemically recurrent prostate cancer as opposed to just using the principles that are used in other disease states. And for that reason, we thought it was very important to delineate some of these considerations in this paper with a group of experts. Davide Soldato: Thanks so much. So, one of the main changes that have been applied in recent years in clinical practice when looking at biochemically recurrent prostate cancer is the use of molecular imaging and particularly of PSMA PET. So, first of all, just a quick question: was the topic of the consensus related on which threshold of PSA to use to order a PET scan to evaluate this kind of patient? David Einstein: Yeah, thanks for that question. It's a super important one. The brief answer is that no, we did not address questions about exactly when clinicians would decide to order scans. We were more concerned with the results of those scans in how you define different disease states. But I think as a broader question, I think a lot of folks feel that finding things on a scan equates that with what we used to find on conventional scans. And fundamentally, we actually sought to redefine that disease space as something that's not equivalent to metastatic disease, and rather coined the term "PSMA-positive BCR" to indicate that traditional BCR prognostic criteria and factors still apply, and that these patients have a distinct natural history from those with more advanced metastatic disease. Ravi Madan: And if I may just add that the National Cancer Institute is running a trial where we're prospectively monitoring PSMA-positive BCR patients. And that data is clearly showing that, much like what we knew about BCR a decade ago, PSMA findings in BCR patients do not change the fact that overall, BCR is an indolent disease state. And the findings, which are usually comprised of five- to seven-millimeter lymph nodes, do not endanger patients or require immediate therapy. And so, while PSMA is a tool that we can be using in this disease state, it doesn't really change the principal approach to how we should manage these patients. And as Dr. Einstein alluded to, there is a drive to create a false equivalency between PSMA-positive BCR and metastatic castration-sensitive prostate cancer, but that is not supported by the data we're accumulating or any of the clinical data as it exists. Davide Soldato: One thing that it's very important and you mentioned in your answer to my question was actually the role of PET scan and conventional imaging, so CAT scan and bone scan that we have used for years to stage patients with metastatic prostate cancer. And you mentioned that there is a distinction among patients who have a positive PET scan and a BCR, and patients who have a positive conventional imaging. And yet, we know that sometimes the findings of the PET scan are not always so clear to interpret. So, I just wanted to understand if the consensus reached an agreement as to when to use conventional imaging to potentially resolve some findings that we have on PET scan among thess patients with BCR? David Einstein: Yeah, I think there's a number of questions actually buried within that question. One of which is: does PSMA PET result in false positives? And the answer has definitely been yes. There's a known issue with false-positive rib lesions. And so, first and foremost, we need to be very careful in calling what truly is suspicious disease and what might actually not be cancer or might be something that is totally separate. So I think that's the first part of the answer to that question. The second is to what extent do we need to use paired PET and conventional imaging to define this disease state? In other words, do you have to have positive findings on one and negative findings on the other in order to enter this definition? The challenge there, as we discussed, is that logistically, oftentimes it's hard to get patients to do multiple sets of scans to actually create that definition. Sometimes it's difficult to get insurers to pay for such scans. And finally, it's hard to sometimes blind radiologists to the results of one scan in reading the other. So, we did have some deliberations about to what extent you could use some of the CAT scan portion of a PSMA PET in order to at least partially define that. We also talked about using bone scans to confirm any bone findings seen on PET. But I think another important part of this is not just the baseline imaging, but also what's going to be done serially on a study in order to define responses and progression. And that's sort of a whole separate conversation about to what extent you can interpret changes in serial PET. Ravi Madan: And just to pick up on the key factor here, I think that the PSMA PET in BCR is pretty good at defining lymph node disease, and that's actually predominantly 80 to 90 percent of the disease seen on these findings. It might be pretty good at also defining other soft tissue findings. The real issues come to bone findings. And one thing the group did not feel was appropriate was to just define only PSMA-positive bone findings confirmed on a CT bone window. There's not really great data on that, but the working group felt that, when in the rare situation, because it is relatively rare, a PSMA-positive finding is in a bone, a bone scan should be done. And it's worth noting that Phu Tran, who is a co-author and a co-leader of this working group, his group has already defined that underlying genomics of conventionally based lesions, such as bone scan, are more aggressive than findings on next-gen imaging, such as PSMA. So, there is also a genomic underlying rationale for defining the difference between what is seen on a PET scan in a bone and what is seen on a bone scan. Davide Soldato: Coming back to this issue of PET PSMA sometimes identifying very small lesions where we don't see any kind of correlates on conventional imaging or where we see only very little alteration on the bone scan or in the CT scan, was there any role that was imagined, for example, for MRI to distinguish this type of findings on the PET scan? Ravi Madan: So, I think that, again, what can be identified on a PSMA frequently cannot be seen on conventional imaging. We didn't feel that it was a requirement to get an MRI or a CT to necessarily confirm the PSMA findings. I think that generally, we have to realize that in this disease state, that questionable lesions are going to be seen on any imaging, including PSMA. We've actually probably put way too much faith in PSMA findings thus far, as Dr. Einstein alluded to with some of the false positives we're seeing. So, I think that these false positives are going to have to be baked into trials. And in terms of clinical practice, it highlights the need to again, not overreact to everything we see and not necessarily need to biopsy everything and put patients' health in jeopardy to delineate a disease that's indolent anyway. Davide Soldato: Thanks so much. That was very clear. So, basically, the main driver was really also the data showing that if we have a BCR, so a patient with a biochemically recurrent disease that is positive on the conventional imaging, this is usually associated with a different aggressiveness of the disease. But coming back to a comment that you made before, Dr. Madan, you said that even if we talk about PSMA-positive BCR, we are still talking about BCR and the same criteria should apply. So, what we have used for years in this space to actually try to stratify the prognosis of patients is the PSA doubling time, so how quickly the PSA rises over time. So, coming back to that comment, was the consensus on the PSA doubling time basically retained as what we were using before, so defining patients with a doubling time less than 12 months, 10 months, 9 months, as patients with a higher risk of progressing in terms of developing metastatic disease? Ravi Madan: Yes, so that's a very important point. And the working group defined high-risk BCR as a PSA doubling time less than six months. And this really comes from Johns Hopkins historical data, which shows that if your doubling time is three months or less, there's about a 67 percent chance of metastasis at five years. If it's between three and six months, it's 50 percent. And if it's over six months, if it's between six and nine months, it's roughly only 27 percent. There are trials that are accruing with eligibility criteria that they may describe as high-risk that are beyond six months, but the data as really it's been defined in the literature highlights that truly high-risk BCR is less than six months. And the working group had a consensus on that opinion, and that was our recommendation. David Einstein: And I think an important follow-on to that is that's regardless of PET findings, right? And so, we present a couple of case studies of patients with positive PET findings who have a long doubling time, in whom the disease is in fact indolent, as you would have expected from a traditional BCR prognostic standpoint. Obviously, there are patients in whom they have fast doubling times, and even if they do not have PET findings, that doesn't make them not high-risk. Ravi Madan: And just to follow up that point, I will let you know a little bit of a free preview that my colleague Melissa Abel from the NCI will be presenting PSMA findings in the context of PSA doubling time at ASCO GU if that data is accepted. Davide Soldato: Looking forward for those data because I think that they're going to clarify a lot of the findings that we have in this specific population. And coming back to one of the points that we made before, so PET PSMA has a very high ability to discriminate also a very low burden of disease, which we currently refer to as oligometastatic biochemically recurrent prostate cancer, which is not entirely defined as an entity. But what we are seeing both in some clinical trials, which use mainly conventional imaging, but also what we're starting to see in clinical practice, is that frequently we use the metastasis-directed therapy to treat these patients. So, just a little bit of a comment on the use of this type of strategy in clinical practice and if the panel thought of including this as, for example, a stratification criteria or mandated in the design of novel clinical trials in the field of BCR? David Einstein: Yeah, I think that's an incredibly important point. You know, fundamentally, there's a lot of heterogeneity in practice where some folks are using local salvage approaches, some are using systemic therapies, in some cases surveillance may be reasonable, or some combination of these different strategies. We certainly have phase two data from multiple trials suggesting that met-directed therapy may help buy patients time off of treatment until subsequent treatments are started. And that in and of itself may be an important goal that we can come back to in discussing novel endpoints. I think what our panel acknowledged was that, in some sense, the clinical practice has gotten even farther ahead than where the data are, and this is being offered pretty routinely to patients in practice. And so, what became clear was that we, in developing clinical trials, cannot forbid investigators from doing something that would be within their usual standard of care, even if it might not be supported by the most robust data. But at minimum, it definitely should be used as a stratification factor, or in some trial designs, you can do met-directed therapy after a primary endpoint is assessed. And that offers a compromise between testing, say, the effect of a systemic therapy but also not excluding patients and investigators from doing what they would have done had they not been on a study. Ravi Madan: And I would just like to follow up your phrasing in the question of "oligometastatic prostate cancer." We have a figure in the paper and it highlights the fact that, unfortunately, that term in prostate cancer is imaging agnostic. And we've already discussed in this podcast, as well as in the paper, that imaging used to define a metastatic lesion, whether it's PSMA or conventional imaging, carries with it a different clinical weight and a different prognosis. So, we feel in the working group, that the correct term for this disease state of PSMA-positive BCR is just that: PSMA-positive BCR. We also have to realize that when we talk about oligometastatic disease, while it's imaging agnostic, it seems to be numerically based, whether it's five or three or 10 depending on the trial. But PSMA-positive BCR does not have a limit in terms of the number of lesions. And so again, we just feel that there is an important need to delineate what we're seeing in this disease state, which again is PSMA-positive BCR, and that should be differentiated frankly from oligometastatic disease defined on other imaging platforms. David Einstein: Right, and that also makes clear that patients can have polyfocal disease on PET that still is not what we would consider metastatic, but goes beyond the traditional definition of oligometastatic. So, in other words, just because someone has PET-detected disease only, that does not automatically equate with oligometastatic. Davide Soldato: Thanks so much. So, you were speaking a little bit, Dr. Einstein, about the different types of treatment that we can propose or not propose to this patient because you mentioned, for example, that in clinical practice MDT, so metastasis-directed therapy, is becoming more and more used. For these patients, we can potentially use systemic treatments, which include androgen deprivation therapy, which can be given continuously or in an intermittent fashion. And recently, we can also use novel systemic therapies, for example, enzalutamide, to treat this type of patient. So, given that the point of the consensus was really to provide consideration for novel clinical trials in this space, what was the opinion on the panel regarding the control arm? So, if we're looking at a novel therapy in the BCR space, does the control arm need to include a therapy or not? And if so, which therapy? David Einstein: Yeah, this is a super important question and one that's subject to a lot of discussion, especially in light of recent data from EMBARK. What we came to a consensus around was the fact that neither MDT nor systemic therapy should be required as a control arm on BCR trials. And we can talk about a number of reasons for that. There's also the pragmatics of what investigators might actually accrue patients to and what they would consider their standard of care, and that's important to factor in, too. I think that one of the major goals of our working group was outlining what kinds of trials we would like to see in the future and where the limitations of the current data stand. For example, EMBARK proposes a strategy of a single treatment discontinuation and resumption at a predefined threshold indefinitely. That's probably not how most people are practicing. Most folks are probably using some version of intermittent therapy as they would have before this trial, but we actually don't have any data supporting that. Moreover, we don't have data comparing different intermittent strategies to one another. We don't know what the right thresholds are, we don't know how much time we buy patients off treatment, and we don't know to what extent MDT modifies that. And so, those are all really important questions to be asking in future versions of these trials. I'd say my second point would be that a lot of drug development is happening with novel therapies that are not hormonal, trying to bring them into this space. And when you think about trying to compare one of those types of therapies to a hormonal therapy on short-term endpoints, the hormonal therapy is always going to win. Hormonal therapy is almost universally effective, it will bring down PSAs, and it will prolong, quote-unquote, "progression." The downside of that is that hormonal therapy doesn't actually modify the disease, it suppresses it, and it tends to have fairly transient effects once you remove it. And so, part of our goal was in trying to figure out some novel endpoints that would allow these novel types of therapies to be examined head-to-head against a more traditional type of hormonal therapy and have some measurement of some of the more long-term impacts. Davide Soldato: So, jumping right into the endpoints, because this is a very relevant and I think very well-constructed part of the paper that you published. Because in the past we have used some of these endpoints, for example, metastasis-free survival, as potentially a proxy for long-term outcomes. But is this the right endpoint to be using right now, especially considering that frequently this outcome is measured using conventional imaging, but we are including in these trials patients who are actually negative on conventional imaging but have a positive PSMA when they enter this type of trial? David Einstein: Yeah, there's a number of challenges with those types of endpoints. One of which is, as you say, we're changing the goalposts a little bit on how we're calling progression. We still don't exactly understand what progression on PET means, and so that's something that is challenging. That said, we're also cognizant of the fact that many times investigators are likely to get PET scans in the setting of rising PSA, and that's going to affect any endpoint that relies purely on conventional imaging. So, there's some tension there between these two different sets of goalposts. One thing that we emphasize is that not only are there some challenges in defining those, but also there're challenges in what matters to a patient. So, if a progression event occurs in the form of a single lesion on a PET scan or even a conventional image, that might be relevant for a clinical trial but might be less relevant for a patient. In other words, that's something that, in the real world, an investigator might use serial rounds of metastasis-directed therapy or intermittent therapy to treat in a way that doesn't have any clinical consequences for the patient necessarily. In other words, they're asymptomatic, it's not the equivalent of a metastatic castration-resistant disease progressing. And so, we also need to be cognizant of the fact that if we choose a single endpoint like PFS, that there's going to be many different versions of progression, some of which probably matter clinically more than others, and some of which are more salvageable by local therapies than others. Ravi Madan: So I think the working group really thoughtfully looked at the different options and underscored perhaps strengths and weaknesses, and I think that's presented as you mentioned in the paper. But I think it's also going to depend on the modality, the approach of the therapeutic intervention. In some cases if it's hormone-based, then maybe PSA is providing some early metrics, maybe metastasis-free survival is more relevant in a continuous therapy, but intermittent therapies might have a different approach. There's emerging immunotherapy strategies, radiopharmaceutical strategies, they might have some more novel strategies as well. I think we have to be open-minded here, but we also have to be very clear: we do not know what progression is on a PSMA scan. Just new lesions may not carry the clinical significance that we think, and we may not know what threshold that ultimately becomes clinically relevant is. So, I do think that there was some caution issued by the working group about using PSMA as an endpoint because we still do not have the data to understand what that modality is telling us. Again, I'm optimistic that the National Cancer Institute's prospective data set that we've been collecting, which has over 130 patients now, will provide some insights in the months and years ahead. Davide Soldato: So, just to ask the question very abruptly, what would you feel like the best endpoint for this type of trials is? I understand that is a little bit related to the type of treatments that we're going to use, whether it's intermittent, whether it's continuous, but do we have something that can encapsulate all of the discussion that we have up until this point? David Einstein: Yeah, so that's a perfect segue to the idea of novel endpoints, which we feel are very important to develop in these novel disease spaces. So, one thing that we discussed was an endpoint called treatment-free survival, which conceptually you can think of as exactly what it sounds like, but statistically you actually have to do some work to get there. And so essentially, you imagine a series of Kaplan-Meier curves overlaid: one about overall survival, one time to next therapy, one time on initial therapy. You can actually then take the area under those curves or between those curves and essentially sum it up using restricted mean survival time analysis. And that can give you a guide about the longitudinal experience of a patient: time spent on treatment versus off treatment; time spent with toxicity versus without toxicity. And importantly, each one of those time-to-event metrics can be adjusted depending on exactly what the protocol is and what is allowed or not allowed and what's prespecified as far as initiation of subsequent therapies. So, we felt that this was a really important endpoint to develop in this disease space because it can really capture that longitudinal aspect. It can really reward treatments that are effective in getting durable responses and getting patients off of therapy, because unfortunately, PFS-based endpoints generally reward more or longer systemic therapy versus shorter or no systemic therapy, and that's sort of an artificial bias in the way those endpoints are constructed. So, I think that there are challenges of course in implementing any new endpoint, and some of the things that are really critical are collecting data about toxicity and about subsequent therapies beyond what a typical trial might collect. But I think in this kind of disease space, that longitudinal aspect is critical because these are really patients who are going to be going through multiple rounds of therapy, going to be going on and off treatments, they're going to be using combinations of local and systemic therapies. And so, any one single endpoint is going to be limited, but I think that really highlights the limitations of using PFS-based endpoints in this space. Ravi Madan: I also think that in the concept of treatment-free survival lies one of the more powerful and, honestly, I was surprised by this, that it was so universally accepted, recommendations from the committee. And that was that the general approach to trials in this space should be a de-escalation of the EMBARK strategy as it's laid out with relatively continuous therapy with one pause. And so, I think again, buried in all of this highlights the need for novel endpoints like treatment-free survival. We get to the fact that these are patients who are not at near-term clinical risk from symptoms of their disease, so de-escalating therapies does not put them at risk. And if you look at, for example, lower-volume metastatic castration-sensitive prostate cancer, it's become realized that we need to de-escalate, and there are now trials being done to look at that. Historically, we know that BCR is an indolent disease process for the vast majority of patients who are not at near-term risk from clinical deterioration. So, therefore, we shouldn't wait a decade into abundant BCR trials to de-escalate. The de-escalation strategy should be from the outset. And that was something the committee really actually universally agreed on. David Einstein: And that de-escalation can really take multiple forms. That could be different strategies for intermittent therapy, different start-stop strategies. It could also mean actually intensifying in the short-term with the goal long-term de-intensification, kind of analogous to kidney cancer where we might use dual checkpoint inhibitors up front with some higher upfront toxicity but with the hope of actually long-term benefit and actually being able to come off treatment and stay in remission. Those kinds of trade-offs are the types of things that are challenging to talk about. There's not a one-size-fits-all answer for every patient. And so, that's why some of these endpoints like treatment-free survival would be really helpful in actually quantifying those trade-offs and allowing each patient to make decisions that are concordant with their own wishes. Davide Soldato: Thanks so much. That was very clear, especially on the part of de-escalation, because, as you were mentioning, I think that we are globally talking about a situation, a clinical situation, where the prognosis can be very good and patients can stay off treatment for a very long period of time without compromising long-term outcomes. And I think that well-constructed de-escalation trials, as you were mentioning and as the consensus endorsed, are really needed in this space also to limit toxicity. This brings us to the end of this episode. So, I would like to thank again Dr. Einstein and Dr. Madan for joining us today. David Einstein: We really appreciate the time and the thought, and I think that even starting these types of discussions is critical. Even just recognizing that this is a unique space is the beginning of the conversation. Ravi Madan: Yeah, and I want to thank JCO for giving us this forum and the opportunity to publish these results and all the expert prostate cancer investigators who were part of this committee. We produced some good thoughts for the future. Davide Soldato: We appreciate you sharing more on your JCO article titled, "National Cancer Institute's Working Group on Biochemically Recurrent Prostate Cancer: Clinical Trial Design Considerations." If you enjoy our show, please leave us a rating and review and be sure to come back for another episode. You can find all ASCO shows at asco.org/podcasts. The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions. Guests on this podcast express their own opinions, experience, and conclusions. Guest statements on the podcast do not express the opinion of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement.  

Have you ever wondered how a family navigates a stage-four cancer diagnosis in their infant with strength and hope? Ryan's son was diagnosed with stage-four cancer shortly after his first birthday. A month before he turned one, a lump in his lower back led to a series of scans. An ultrasound and MRI revealed multiple tumors, confirming the devastating diagnosis. In this episode, Ryan shares the family's journey through integrative and conventional healing approaches. His story offers hope, clarity, and empowerment to anyone facing a similar path. Looking for a natural way to support your cellular health? Shop "Our 7" supplement with 7 botanicals and minerals for oxidation and inflammation reduction support use code BYDESIGN to save 10%: https://ourhealthnaturally.com?sca_ref=10078968.EUqhSBYDNdbJ In this episode, you'll learn: ⏰    00:00 - Introduction ⏰    03:12 - Discovering Ryder's tumor and stage 4 Neuroblastoma ⏰    12:20 - Blending chemo with integrative healing at home ⏰    20:13 - Cancer today and the power of foundations ⏰    23:11 - Cancer as disconnection from nature ⏰    31:54 - Cancer support community and the birth of The Stern Method ⏰ 1:00:28 - The ONE thing you can do to activate self-healing Check out  Ryan Sternagel's Bio: Ryan Sternagel is the co-founder along with his wife Teddy of The Stern Method, a platform informing and inspiring families preventing or reversing cancer to succeed on all fronts. In May of 2014 their son Ryder was diagnosed with stage four neuroblastoma, a childhood cancer of the nervous system, eleven days before his first birthday.  Through an integrative approach leaving nothing on the table, and ridding their lives of all possible causes, including building a non-toxic house in the middle of the woods, today Ryder is thriving. Through continually seeking out and interviewing the world's top integrative cancer doctors to stay up to date, their Going Integrative Plus member community, and Our Health Naturally supplement line, The Sternagels have committed their lives to making healing and prevention easier for others than it was for them.

Put your mind at ease by praying and singing the Word of God – try it today. -------- Thank you for listening! Your support of Joni and Friends helps make this show possible.     Joni and Friends envisions a world where every person with a disability finds hope, dignity, and their place in the body of Christ. Become part of the global movement today at www.joniandfriends.org   Find more encouragement on Instagram, TikTok, Facebook, and YouTube.

Send us a text if you want to be on the Podcast & explain why!Article mentioned in the podcast: https://www.painscience.com/blog/corrective-exercise-trap.htmlNext SUF weekend seminars: DUMBO NYC 2-20/21, Houston 13/14, Oakland 27/28.Q2 schedule will be released soon!What happens when a client shoves a scary MRI report in front of you and waits for your take? We turn that tense moment into a masterclass on scope, language, and movement by breaking down C6–C7 findings, clarifying true red flags, and showing how a quick consult with a physical therapist can transform fear into a clear plan. Instead of hiding behind corrective exercise acronyms, we lay out a practical path to real credibility: anatomy fluency, precise coaching, and a trusted referral network.We walk through how to read common cervical terms without stepping outside your scope, why peripheral symptoms like numbness or burning change the plan, and how to use the biopsychosocial model to reduce threat and improve outcomes. You'll hear a simple, effective session flow—brief targeted correctives followed by progressive overload with squats, step-ups, rows, planks, and face pulls—designed to build capacity without provoking symptoms. The emphasis is on clarity over complexity: coach scapular protraction and retraction, cue depression instead of shrugging, and modify load and positions based on feedback, not fear.Along the way, we question the corrective exercise trap, the posture hype cycle, and the industry's obsession with letters over leadership. Real leverage comes from hands-on education and an active network of DPTs and specialists who answer the phone, share nuance, and send referrals. If you want clients to see you as the professional who brings calm, clarity, and results, start by mastering anatomy, speaking hope, programming for strength, and knowing exactly when to refer. Subscribe, share this with a coach who needs it, and leave a review telling us the one skill you're doubling down on this month.Want to become a SUCCESSFUL personal trainer? SUF-CPT is the FASTEST growing personal training certification in the world! Want to ask us a question? Email info@showupfitness.com with the subject line PODCAST QUESTION to get your question answered live on the show! Website: https://www.showupfitness.com/Become a Successful Personal Trainer Book Vol. 2 (Amazon): https://a.co/d/1aoRnqANASM / ACE / ISSA study guide: https://www.showupfitness.com

In this episode, Therese Markow, Dr. Catherine Lebel, and Dr. Sam Nivins discuss the impact of prenatal factors on fetal brain development. Catherine explains how MRI can detect subtle brain changes due to prenatal alcohol exposure, even at low levels, and emphasizes the importance of avoiding alcohol during pregnancy. Sam discusses the effects of maternal obesity before pregnancy on brain development, noting sex-specific differences and the importance of early intervention. Both also touch on the impact of stressors, such as natural disasters, and the need for early identification and support for children with potential reading difficulties.     Key Takeaways: Even exposing a fetus to one alcoholic drink per week during pregnancy shows a detectable difference in brain structure compared to kids who had no alcohol exposure at all.  The same is true of prenatal maternal obesity, even if the obesity is preconceptional. Reading is a skill that must be taught to children. Prereading skills lay the foundation for later reading. And prereading skills can be visualized with brain imaging. When you know what part of the brain is affected, you can better tailor interventions to target those particular consequences.   "People who have good support from a partner or other folks in their lives, not only do they tend to do better, but their kids tend to do better too." —  Dr. Catherine Lebel   Connect with Dr. Lebel and Dr. Nivins  Dr. Lebel's Professional Bio & Publications: https://profiles.ucalgary.ca/catherine-lebel  Dr. Nivins' Professional Bio & Publications: https://ki.se/en/people/samson-nivins  Website: https://www.developmentalneuroimaginglab.ca/    Connect with Therese: Website:  www.criticallyspeaking.net Bluesky: @CriticallySpeaking.bsky.social Instagram: @criticallyspeakingpodcast Email: theresemarkow@criticallyspeaking.net   Audio production by Turnkey Podcast Productions. You're the expert. Your podcast will prove it. 

Brent Ness, CEO and President of Aclarion,  highlights the challenges of diagnosing and treating chronic lower back pain, a leading driver of healthcare costs and opioid addiction. Traditional MRI and CT imaging do not reveal the biochemical source of pain within spinal discs, leading to misdiagnosis and unsuccessful treatment. The Aclarion technology uses MR spectroscopy to measure pain-causing biomarkers and, through a cloud-based, AI-powered SaaS model, analyzes the raw data and sends the physician a report within minutes. Brent explains, "The diagnosis and accurate treatment planning of back pain are incredibly complex. And when you think about pain management physicians, rehab, all the way up to spine surgery identifying the source of pain accurately leads to better treatment and then obviously better outcomes.There are 266 million people around the world who suffer from chronic low back pain. So I'm not talking about the kind that you had a rough weekend skiing, golfing, or hiking, and you're a little sore. I'm talking about the kind that keeps people from participating in a meaningful life. " "When you think about the joints and the sources of blood flow, the nerves that are all around your spinal cord, the vertebral columns, and there's just a lot of moving parts and a lot of really, let's just call it high-value real estate that can actually be the source of pain. And really, our superpower is to help physicians see the invisible. Meaning that normally when you go to the doctor, and they do a workup on you, they'll use an MRI or a CT scanner. And those modalities are really good at pinpointing anatomical issues that might be causing pain. What we do is we use MR spectroscopy, not to make a picture of your back, but rather to measure the biomarker content inside the discs that are invisible on a normal MRI. And as it turns out, what's inside your disc can actually be the source of pain." #ACON #CLARITYtrial #lowbackpain #spinesurgery #MRSpectroscopy #Biomarkers #AugmentedIntelligence #innovation #ChronicPain #BackPain #MedicalTechnology #AI #HealthcareInnovation #SpineCare #MRI #PainManagement #DigitalHealth #Diagnostics #HealthTech #PatientCare aclarion.com Download the transcript here

Brent Ness, CEO and President of Aclarion,  highlights the challenges of diagnosing and treating chronic lower back pain, a leading driver of healthcare costs and opioid addiction. Traditional MRI and CT imaging do not reveal the biochemical source of pain within spinal discs, leading to misdiagnosis and unsuccessful treatment. The Aclarion technology uses MR spectroscopy to measure pain-causing biomarkers and, through a cloud-based, AI-powered SaaS model, analyzes the raw data and sends the physician a report within minutes. Brent explains, "The diagnosis and accurate treatment planning of back pain are incredibly complex. And when you think about pain management physicians, rehab, all the way up to spine surgery identifying the source of pain accurately leads to better treatment and then obviously better outcomes.There are 266 million people around the world who suffer from chronic low back pain. So I'm not talking about the kind that you had a rough weekend skiing, golfing, or hiking, and you're a little sore. I'm talking about the kind that keeps people from participating in a meaningful life. " "When you think about the joints and the sources of blood flow, the nerves that are all around your spinal cord, the vertebral columns, and there's just a lot of moving parts and a lot of really, let's just call it high-value real estate that can actually be the source of pain. And really, our superpower is to help physicians see the invisible. Meaning that normally when you go to the doctor, and they do a workup on you, they'll use an MRI or a CT scanner. And those modalities are really good at pinpointing anatomical issues that might be causing pain. What we do is we use MR spectroscopy, not to make a picture of your back, but rather to measure the biomarker content inside the discs that are invisible on a normal MRI. And as it turns out, what's inside your disc can actually be the source of pain." #ACON #CLARITYtrial #lowbackpain #spinesurgery #MRSpectroscopy #Biomarkers #AugmentedIntelligence #innovation  #ChronicPain #BackPain #MedicalTechnology #AI #HealthcareInnovation #SpineCare #MRI #PainManagement #DigitalHealth #Diagnostics #HealthTech #PatientCare aclarion.com  Listen to the podcast here

Robert Kurland, Ph.D.Can AI Have a Soul? What Science Fiction SaysDr. Robert Kurland, a convert to Catholicism in 1995, is a retired physicist who has applied magnetic resonance to problems of biological interest in his research (web search: “Kurland-McGarvey Equation”). Dr. Kurland is a graduate of Caltech (BS, 1951, “with honor”) and Harvard (PhD, 1956). His scientific career at Carnegie-Mellon, SUNY/AB, Cleveland Clinic, Geisinger Medical Center, has focused on biological applications of magnetic resonance, including MRI. Since his conversion to Catholicism, he has tried to spread the message that there's no war between Catholic teaching and science.AbstractMuch before AI tools became available, science fiction stories had shown how it might be manifested in computers, robots, and humanoid androids. As with other Speculative Fiction (Tolkien, C.S. Lewis) one takes the contrapositive beings and situations in such tales not as possible reality, but as parables illustrating the human condition. Three stories will be discussed: “Deus X” in which human consciousness can be transplanted to computers as life after death“The Measure of a Man—Star Trek, Next Generation,” a trial to determine whether the android Data is more than a machine “Our Lady of the Artifacts,” a novel in which an android with superhuman capabilities is possessed by a devilFr. Robert J. Spitzer, S.J., Ph.D.Why AI Can't Have a Soul: The Transphysical ParadoxFor more on Magis AI, see https://wcatradio.com/wp-content/uploads/2026/02/MagisAI.pdfFr. Robert J. Spitzer, S.J., Ph.D. is President of the Magis Center of Reason and Faith (magiscenter.com), one of the largest science, faith, and reason apologetics institutes in the world. He was President of Gonzaga University from 1998 to 2009, where he increased the student body by 75%, oversaw the construction of 20 new facilities, and raised $200+ million for scholarships and buildings. He is the author of nineteen books, including the award-winning books New Proofs for the Existence of God and Science, Reason, and Faith: Discovering the Bible. He has also authored many scholarly articles on faith and science, metaphysics, and happiness and ethics. Father Spitzer has his own weekly EWTN television show called Fr. Spitzer's Universe. He has appeared on the Larry King Show (in discussion with Stephen Hawking and Deepak Chopra), the History Channel, the Today Show, and a PBS series. He started seven institutes dedicated to faith and reason and happiness/purpose in life. He was a professor at Georgetown University, Seattle University, and Gonzaga University and was awarded the teaching medal at both Georgetown University and Seattle University. He has held two major academic chairs—the Frank Shrontz Endowed Chair in Professional Ethics (Seattle University) and the John L. Aram Chair of Business Ethics (Gonzaga University), and has won multiple academic and professional awards including the DeSmet Medal (Gonzaga University's highest award), the Aquinas Medal (for Catholic philosophical scholarship), honorary doctorates, Phi Beta Kappa (honorary), and professional society awards.AbstractThe human soul performs five functions that cannot be reduced to physical processes and structures: (1) Self-consciousness, (2) Abstract intellection through conceptual ideas, (3) Conscience and moral awareness, (4) Transcendental awareness, and (5) Spiritual-numinous awareness. Since AI is reducible, and will always be reducible to physical processes and structures, AI will not replace a human soul – or be like a human soul.

In this emergency injury update episode of The Pacers Post Up, Brad and Ryan break down the heartbreaking news rocking the Indiana Pacers: second-year forward Johnny Furphy has suffered a torn right ACL. Per Michael Scotto of Hoops Hype, Furphy underwent an MRI in New York today after awkwardly landing following a dunk in Sunday's loss to the Toronto Raptors. The results confirmed the tear, sidelining the promising Aussie swingman for the remainder of this already injury-ravaged season. This is yet another devastating blow for a Pacers team that's been decimated by injuries. We discuss what Furphy's absence means for the lineup, his breakout flashes this year, the mounting frustration in Indy, and whether this cursed season can find any silver lining. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Midlife health decisions rarely fail because women “don't know what to do.” They fail because the stakes change overnight, the calendar stays overloaded, and the system you used to rely on stops working.This conversation sits at the intersection of two realities: breast cancer can show up even without family history, and the perimenopause to menopause transition forces a new level of precision around hormones, bone health, fatigue, and what you put on your skin.In this episode, Sally Mueller, co-founder of Womaness, speak candidly from lived experience—diagnosis timelines, treatment tradeoffs, dense breast screening gaps, and the unglamorous but decisive habits that actually keep women on track.Timestamps(03:16) Following instincts as an early prevention strategy (11:18) Clean, hormone-free formulations and long-term exposure risk (12:58) Hereditary versus environmental drivers of breast cancer (20:20) Dense breast tissue and proactive screening strategies (27:31) Vitamin D deficiency and systemic fatigue signals (28:49) Supplement consistency versus reactive use (32:32) Why steady supplementation outperforms short-term fixes (36:18) Bone health through impact, resistance, and movement variety (40:07) Exercise variation as a stimulus for bone remodeling (41:47) Treating exercise like a non-negotiable meeting Guest BioSally Mueller — Co-Founder and CEO, WomanessSally Mueller is the co-founder of Womaness, a women's wellness brand focused on perimenopause and menopause solutions across skin, body, supplements, and sexual wellness.LinkedIn: https://www.linkedin.com/in/sally-mueller/Key PointsMidlife health breakdown is often a systems failure, not a motivation problem: Delayed screenings, inconsistent supplements, and deprioritized movement compound risk over time.Early detection depends on follow-through, not awareness: Dense breast tissue, hormone shifts, and missed baselines create blind spots when care is delayed.Consistency beats intensity in supplements and exercise: Vitamin D, bone-loading movement, and simple routines outperform sporadic “health resets.”Clean inputs matter more after cancer, but should start earlier: What women put on and in their bodies becomes more consequential during hormonal transition.Exercise functions as prevention infrastructure, not lifestyle garnish: Impact, resistance, and aerobic movement materially affect recurrence risk, bone density, and fatigue.Deep DivesDelayed care as a compounding risk factorMissed appointments increase exposure windowsDelays often happen during peak hormonal volatilityDense breast tissue and the screening gapMammograms alone can miss early signalsUltrasound and MRI baselines improve detectionVitamin D deficiency as a hidden performance drainFatigue and joint pain can signal depletionWinter and low sun accelerate declineSupplement discipline versus reactive useInconsistent intake reduces benefitFewer supplements taken regularly outperform complex stacksBone health beyond medicationImpact and resistance stimulate bone remodelingMovement variety matters more than volumeExercise as a protective interventionAerobic activity reduces systemic disease riskStrength work supports bone and joint resilienceClean formulations and cumulative exposureHormone-free products reduce added loadTransparency matters more during midlife transitionsWhy midlife routines collapse firstCaregiving, careers, and stress convergeHealth behaviors are usually the first to dropTreating exercise like a meetingScheduled movement increases adherenceNon-negotiable time blocks protect consistencyPrevention as an operating modelMidlife health requires durable systemsShort-term fixes fail under long timelinesLinks & ReferencesBreast cancer screening beyond mammography (Mayo Clinic): https://www.mayoclinic.org/tests-procedures/mammogram/in-depth/breast-cancer/art-20047233Vitamin D deficiency, symptoms, and testing (National Institutes of Health): https://ods.od.nih.gov/factsheets/VitaminD-Consumer/Exercise and bone health in midlife and beyond (International Osteoporosis Foundation): https://www.osteoporosis.foundation/health-professionals/prevention/exercise

Jason Smith, Mike Harmon and FOX Sports 1 NBA analyst Ric Bucher put a bow on an insanely busy trade deadline. And If Luka Doncic, who's set for an MRI, is out for a while, can the Lakers survive without him?See omnystudio.com/listener for privacy information.

Brought to you by TogetherLetters & Edgewise!In this episode: Ikea's next cheap Bluetooth speaker is a playful purple mouseHow does Lemonade Autonomous Car insurance work?Elon Musk's SpaceX to merge with xAIMusk's SpaceX and xAI merge to make world's most valuable private companyChina bans all retractable car door handles, starting next yearRevolutionary Cryogenic Coolant Uses Abundant Elements, Eliminates Need for Rare-Earths in MRI and Quantum CoolingMeta Quest 3 Gets A Futuristic New FeatureSpotify Just Added Three New Lyrics Features, Including One I've Been Dying ForWeird and Wacky: NASA will finally allow astronauts to bring their iPhones to spaceTech Rec:Sanjay - Grayl Adam - Reading RefreshFind us here:sanjayparekh.com & adamjwalker.comTech Talk Y'all is a proud production of Edgewise.Media

In this episode, Dr. Rena Malik, MD sits down with Dr. Scott Eggener to explore the nuanced landscape of prostate cancer screening, diagnosis, and treatment. Together, they discuss prevention strategies, highlight the evolving role of exercise and supplements, and clarify the latest advances in biopsy and therapy options—all while emphasizing data-driven, individualized patient care. Listeners will gain essential insights on making informed choices about prostate health, screening practices, and the importance of shared decision-making. In this video discussion,  Become a Member to Receive Exclusive Content: renamalik.supercast.com Schedule an appointment with me: https://www.renamalikmd.com/appointments ▶️Chapters: 00:00:00 Introduction 00:00:01 Prostate cancer overview 00:05:32 Prostate cancer prevention 00:09:29 Supplements and vitamins 00:15:24 Medications and prostate cancer risk 00:25:35 Prostate cancer screening guidelines 00:37:55 PSA, markers, MRI, biopsy 00:44:53 Gleason 6 cancer management 00:58:29 Surgery vs. radiation comparison 01:10:49 Side effects of treatment 01:35:45 Testicular and kidney cancer Let's Connect!: WEBSITE: http://www.renamalikmd.com YOUTUBE: https://www.youtube.com/@RenaMalikMD INSTAGRAM: http://www.instagram.com/RenaMalikMD TWITTER: http://twitter.com/RenaMalikMD FACEBOOK: https://www.facebook.com/RenaMalikMD/ LINKEDIN: https://www.linkedin.com/in/renadmalik PINTEREST: https://www.pinterest.com/renamalikmd/ TIKTOK: https://www.tiktok.com/RenaMalikMD ------------------------------------------------------ DISCLAIMER: This podcast is purely educational and does not constitute medical advice. The content of this podcast is my personal opinion, and not that of my employer(s). Use of this information is at your own risk. Rena Malik, M.D. will not assume any liability for any direct or indirect losses or damages that may result from the use of information contained in this podcast including but not limited to economic loss, injury, illness or death. Learn more about your ad choices. Visit megaphone.fm/adchoices

For the next few weeks, the guys will be re-airing some of their favourite episodes from our archives.Since March has been endometriosis awareness month, on today's episode, Asif and Ali welcome gynecologist Dr. Sonhy Singh to discuss this common and often debilitating disease.. Dr. Singh explains what it is and how it affects at at least 10% of women. He discusses how it is often underdiagnosed or misdiagnosed how this delay in diagnosis can have long term impacts. He then discusses some tests that can be used in the diagnosis, while acknowledging that in the past endometriosis has easily been missed on ultrasounds and MRI's. He talks about how it can be treated and the severe complications he has seen in some women. Finally, Dr. Singh discusses the importance of social media in the advocacy of this disease and celebrities that have been affected by it. The opinions expressed are those of the hosts, and do not reflect those of any other organizations. This podcast and website represents the opinions of the hosts. The content here should not be taken as medical advice. The content here is for entertainment and informational purposes only, and because each person is so unique, please consult your healthcare professional for any medical questions. Music courtesy of Wataboi and 8er41 from PixabayContact us at doctorvcomedian@gmail.comFollow us on Social media:Twitter: @doctorvcomedianInstagram: doctorvcomedian Hosted on Acast. See acast.com/privacy for more information.

Hello, all you and the Relentless Health Tribe trying to figure out how to do right by patients and the folks footing the bill. Welcome to it. This is episode 499, one episode before episode 500. So, come back next week for that one. For a full transcript of this episode, click here. If you enjoy this podcast, be sure to subscribe to the free weekly newsletter to be a member of the Relentless Tribe. All right, so today, let's talk about the inches that are all around us. Let's find some. Musculoskeletal spend, otherwise known as MSK spend, for any given plan sponsor adds up to the tune of something like 20% or 30% of total plan spending, depending on the member demographic. MSK rolls in at $16 PMPM, I just saw, according to a report Keith Passwater sent me a couple of weeks ago. It's the third most costly spend apparently overall. And it's easy to see why, right? On any given day, odds are good any given plan member is gonna do something that, in hindsight, was fairly obviously a bad idea and wind up getting hurt in some low-acuity way. For example, I remember that one time I twisted my ankle on a curb getting outta my car. Given the right space, enough time, and concentration, I can do the worst parking job you've ever seen in your life and manage to twist my ankle in the process. But I digress. Here's the point. MSK spend adds up really fast. Add to that something like 50% of spine surgeries are said to be unnecessary. The same thing goes true from injuries like twisted ankles, for example, that would have healed themselves without an ER visit, without any intervention aside from ice, rest, and elevate. Because it turns out that something like 80% of those twisted-ankle, banged-up-the-back types of MSK injuries are actually low acuity, and a huge percentage of those will heal by themselves. On that point, let me bring in some context here, some late-breaking news. I was reading Dana Prommel's newsletter. She wrote, and I'm reading this, she wrote, "The 2026 National Healthcare Expenditure data reports are out, and it is another sobering reflection of our current system. Personal healthcare spending has surged by over 8%, and our healthcare spend as a share of the GDP has followed that same aggressive trajectory." Then Dana writes, "The most troubling takeaway from the 2026 report is the lack of a 'health dividend.' Despite [this] 8% increase in spending, we aren't seeing a corresponding 8% increase in longevity, wellness, or chronic disease management. People aren't getting significantly healthier; they are just getting more 'care.' And that 'care' isn't always good care, or the right care, or care by the right type of clinician, at the right time, in the right setting." Is that not the perfect segue or what? Because this is what we're talking about on the show today in regard to, again, MSK care—care that can wind up costing millions of dollars across plan members, and it might be unnecessary because, again, the twisted ankle or the pain in the lower back would have healed itself without any care, without an ER visit. But if an ER visit was had, that patient probably is gonna wind up with a bunch of imaging. Probably is gonna wind up with a referral to a surgeon. And now there's a surgery scheduled, and the patient has been off work for however long all that took. There's a lot of direct and indirect costs that may or may not add up to any given health dividend or health span or whatever you wanna call it—better quality of life.   Why does all this happen? How does it happen? One reason is what Dr. Jay Kimmel calls the white space of MSK care. This is where a patient does a truly breathtaking job parking the car, twists her ankle, starts to swell up, and now a decision has to be made: Go to the ER. Go to urgent care. Go home. Or what if it's a parent making this choice for a kid? In the olden days, maybe that patient would've called up his or her longtime family doctor and asked what to do, and maybe if that longtime family doctor didn't know, he or she would have called up the local ortho and gotten their opinion. Or maybe the two were sitting together in the doctor's lounge at the time, or maybe they rounded together in the hospital and, and, and … There used to be lots of opportunities for spontaneous questions and answers and curbside consults. But not today most of the time, really, unless you're a patient with a doctor in the family. But even for a PCP, who wants an ortho consult? Amy Scanlan, MD, and I discussed this quite a bit in an earlier episode (EP402). There's no doctor lounges anymore. There's no coffee klatch down in radiology either. There's just a lot of cultural shifts, in other words. But all of this, everything I have said thus far, all adds up to one big takeaway: These excess costs that don't have commensurate improved clinical outcomes, they happen because patients are on their own to triage themselves. They look at their black-and-blue whatever, or they're standing there listening to their kid cry and they are deciding what to do. And the thing is, if they choose the ER—because, again, they don't have a doctor, anybody they can just call with the right kind of clinical background—once they head into that ER and sit there for six hours and demand an MRI because now it has to be worth their time because they sat there for six hours; but now there's a false positive and the ER docs are being conservative because of malpractice or whatever and they refer them to some sort of surgeon … Look, everybody's doing their best with the information that they have at the time, but you can see how easy it is for a person to avoidably wind up costing a lot of money for a musculoskeletal injury that would have healed by itself. So, yeah, let's talk about how we can get patients some help in that so-called white space. How can we get them, triage before the triage, as I managed to say more than once in the conversation that follows? Let's get them on a good trajectory to start. Today, my guest is Dr. Jay Kimmel. Dr. Kimmel is an orthopedic surgeon, and he's been in practice in Connecticut for over 35 years. He and Steve Schutzer, MD, co-founded Upswing Health. I talked with Dr. Steve Schutzer about Centers of Excellence in an earlier episode (EP294). Upswing Health provides members with the opportunity to talk with an athletic trainer within 15 minutes and an orthopedic specialist within 24 hours. So, instead of having a panic attack of indecision and ultimately winding up in the ER, getting coughed on in the waiting room, members have somebody helping them in this white space so they can get triaged before the triage. I need to thank Upswing Health. I am so appreciative they donated some financial support to cover the costs of this episode. This podcast is sponsored by Aventria Health Group with an assist from Upswing Health. Also mentioned in this episode are Upswing Health; Keith Passwater; Dana Prommel; Amy Scanlan, MD; Steve Schutzer, MD; Eric Bricker, MD; Al Lewis; Nikki King, DHA; Matt McQuide; Christine Hale, MD, MBA; and Chris Deacon. For a list of healthcare industry acronyms and terms that may be unfamiliar to you, click here.   You can learn more at upswinghealth.com and follow Dr. Kimmel on LinkedIn.   Jay Kimmel, MD, is the president and co-founder of Upswing Health, the country's first virtual orthopedic clinic. He founded Upswing with Steve Schutzer, MD, to rapidly assess, triage, and manage orthopedic conditions in a cost-effective, high-value manner, helping patients avoid unnecessary imaging, procedures, and delays in care. Dr. Kimmel had a long and distinguished career as a practicing orthopedic surgeon with Advanced Orthopedics New England. He earned his undergraduate degree from Cornell University and his medical degree from the University of Rochester. He completed his orthopedic residency at Columbia Presbyterian Medical Center, where he trained with leaders in shoulder surgery, followed by a sports medicine fellowship at Temple University Center for Sports Medicine, where he participated in the care of Division I collegiate athletes. He is board-certified in orthopedic surgery and is a Fellow of the American Academy of Orthopedic Surgeons. Dr. Kimmel specializes in sports medicine with an emphasis on shoulder and knee injuries and holds a subspecialty certificate in orthopedic sports medicine from the American Board of Orthopedic Surgery. He is also a member of the American Orthopedic Society for Sports Medicine. Dr. Kimmel co-founded the Connecticut Sports Medicine Institute at Saint Francis Hospital, a multidisciplinary center dedicated to providing high-quality care for athletes at all levels, and served as its co-director for many years. He has a strong commitment to education and served for over 20 years as an assistant clinical professor in both family medicine and orthopedics at the University of Connecticut. He has also served as a team physician at the professional, collegiate, and high school levels.   07:49 EP472 with Eric Bricker, MD, on high-cost claimants. 08:01 What is the "white space" in MSK spend? 10:43 Statistics on Connecticut's spending on plan members with low-acuity MSK injuries. 13:30 How back pain also easily transitions from a low-acuity issue to a high-acuity problem. 15:11 How plan sponsors can detect their white space downstream spend. 16:58 EP464 with Al Lewis. 17:02 EP470 with Nikki King, DHA. 18:15 Why where patients start their journey often dictates where they wind up and how costly that medical pathway is. 20:48 Where PCPs fit into this MSK spend issue. 25:26 EP468 with Matt McQuide. 25:34 EP471 with Christine Hale, MD, MBA. 25:39 Why access is key.   You can learn more at upswinghealth.com and follow Dr. Kimmel on LinkedIn.   Jay Kimmel, MD, of @upswinghealth discusses #MSKspend on our #healthcarepodcast. #healthcare #podcast #financialhealth #patientoutcomes #primarycare #digitalhealth #healthcareleadership #healthcaretransformation #healthcareinnovation #musculoskeletal   Recent past interviews: Click a guest's name for their latest RHV episode! Mark Noel, Gary Campbell (Take Two: EP341), Zack Kanter, Mark Newman, Stacey Richter (INBW45), Stacey Richter (INBW44), Marilyn Bartlett (Encore! EP450), Dr Mick Connors  

Think Radiology is just about pushing buttons and taking pictures? Think again. In this episode of A Couple of Rad Techs Podcast, Chaun gets "100% real" about why she stumbled into this profession—it wasn't a "calling," it was a quest for independence and a career that didn't involve mucus. We explore how a 20-year career in radiologic technology can be a chameleon, allowing you to move from X-ray and CT into informatics, education, and even becoming a published author.In this episode, we discuss:The Launchpad Effect: Why medical imaging is a gateway to the entire medical world, not just a clinical job.The "Roommate Factor": How this career provides the financial foundation for independence and a lifestyle you love.Endless Modalities: A breakdown of paths from MRI and Mammography to Radiation Safety and Forensics.Beyond the 9-to-5: The truth about schedules, travel work, and avoiding the "Sunday scaries."Resources Mentioned:A Couple of Rad Techs Podcast: Dive into past episodes about informatics and specialized modalities.Career Questions? Drop your thoughts in the comments or reach out for a roadmap on how to start your own pivot.Radiologic Technologist Career, Rad Tech Salary, Radiology School Tips, Medical Imaging Pivot, ASRT Leadership, MRI and CT Modalities, Informatics in Radiology, Healthcare Career Freedom.

On today's episode, we discuss James's latest adventures with his Tesla, including how it handles blind pedestrians, misreads faded stop lines, learns to dodge potholes, and occasionally blasts through a Ruston speed trap at 47 in a 35 while he scrambles to correct it. The “fearsome threesome” compare Tesla's different driving modes (from chill to “Mad Max”), explain how Smart Summon and “ASS mode” (Actually Smart Summon) train the car in private lots, and argue that human drivers make far deadlier mistakes even if the car's errors are more noticeable. The conversation then jumps to AI agents, with Mark describing how a Claude-based agent framework accidentally spawned a million‑agent, AI‑only social network that began forming its own “culture,” raising questions about runaway compute costs and what happens when software mostly talks to itself. From there, they dig into data centers and energy: Meta's massive new facility and land buy near Holly Ridge, talk of moving AI compute to space using solar power, and concern over how much national‑debt‑scale capital big tech and Apple (via its QAI acquisition) are about to pour into advanced models and audio “earables.” On the medical front, they highlight emerging tech like MRI-guided cryo-freezing of tumors, speculative “earable” devices that can monitor vitals and deliver drugs, and overhyped claims about brain stimulation that could allegedly “upload” piano pieces or martial arts skills into your nervous system. The episode closes with Bitcoin: they note its slide from around 126,000 to under 70,000, debate four‑year halving cycles, deflationary pressure from AI, the risks of short selling versus prediction markets, and end with the idea that if listeners dabble in crypto at all, it should be for fun money only—not because of anything they hear on this show. Don't miss it!

Guest: Dr. Lee Warren, Board-Certified Neurosurgeon Introduction: What if you could change your brain by changing your mind? Board-certified neurosurgeon Dr. Lee Warren joins us to reveal something revolutionary: you're not stuck with the brain you have. Through groundbreaking neuroscience research, we now know that what you repeatedly think about literally restructures your brain. For parents learning to live from agency instead of control, understanding what's actually happening in your nervous system changes everything. Dr. Warren's new book, The Life-Changing Art of Self-Brain Surgery: Connecting Neuroscience and Faith to Radically Transform Your Life, releases February 3, 2026. You're in the right place if: You wonder why you keep defaulting to control, even when you want to parent from peace It feels impossible to break old patterns even though you know the truth Your child struggles with thoughts like "I'm stupid," "I'll never learn," or "everyone else can do this but me." You avoid letting your kids struggle because you want to protect them from pain You want to understand the neuroscience behind why fear-based parenting creates control operating systems in your children You're ready to break generational patterns of fear and shame in your family Episode Highlights: Mind vs. Brain - The Revolutionary Truth Traditional neuroscience has taught that your brain generates everything about you—your personality, memories, even your sense of having a mind. But here's the problem: there's no actual science proving this is true. It's just a theory. Through functional MRI imaging developed around 2000, we can now see what really happens: your mind directs your brain, not the other way around. Your brain is like your kidneys or heart—an organ that carries out the interaction of your mind with the world. This changes everything. The Neuroscience of Fear vs. Gratitude When you're afraid, your amygdala (a walnut-sized area in your limbic system) triggers fight-or-flight responses. It's tiny and can't think well—it can only react. But your hippocampus acts like a one-way switch: it either triggers your amygdala OR your frontal lobes (billions of neurons designed for rational thinking). The deciding factor? Fear or gratitude. You literally cannot be grateful and anxious at the same time. This is exactly what Paul described in Philippians 4:6-8 two thousand years ago: "Don't be anxious, be grateful instead...think about what's noble, true, lovely..." Paul was 2,000 years ahead of neuroscience. The Auburn University Discovery Dr. Warren shares the pivotal moment at Auburn University's MRI Research Center when he and his wife Lisa watched a patient's brain respond to different thoughts in real-time. When thinking about the worst day of her life, her amygdala lit up, blood pressure rose, heart rate increased. When thinking about her happiest memory, frontal lobes activated, peace indicators appeared, blood pressure and heart rate dropped. That's when God spoke to Dr. Warren: "When you do surgery, you intentionally make a structural change in someone's brain to improve their life. When someone changes from harmful thoughts to helpful thoughts, they're also intentionally making structural changes in their brain to improve their life. That's surgery too—self-brain surgery." The Power of Anti-Fragility We've been taught that humans are fragile—easily broken and needing protection. But Scripture, neuroscience, psychology, and social science all agree: we're actually anti-fragile. You can't be as strong as you're capable of being without being broken a few times along the way. Romans 5:3-5 explains the process: suffering produces endurance, endurance produces character, and character produces hope. Your mid-anterior cingulate (the part of your brain that handles willpower and resilience) literally gets stronger when you do hard things you don't want to do. George's Story - From Dyslexia to Fearless Dr. Warren's 7-year-old grandson George couldn't read despite being brilliant at everything else. He was diagnosed with dyslexia and worked with a tutor for 8 months, making up 3 grade years in reading. When George called his grandfather and said, "Pop, I'm a reader!" everyone wept. But here's the lesson: George is now fearless at age 10 because he faced the hardest thing in his life—not being able to read—and overcame it. If his parents had blamed the school or lowered standards, George would still be afraid of things he doesn't know how to manage. Instead, he knows nothing in his entire life will be as hard as learning to read, and he did it anyway. Mary's Story - From "I'm Stupid" to Syracuse Graduate Janet shares about 10-year-old Mary who had every learning label and refused to pick up a pencil or book. When learning to type, every mistake beep triggered outrage: "I'm stupid, I'll never learn, you hate me." After 3 days, Janet transcribed Mary's words on a whiteboard and asked, "Can we call this list 'lies'?" They created a truth list: next to "I'm stupid" was "I'm capable," next to "you hate me" was "you believe in me." Mary's new instruction: every time she heard the beep, name the truth. Beep. Truth. Beep. Truth. Struggle, truth. In 3 weeks, Mary typed 35 words per minute with 98% accuracy. She recently graduated from Syracuse University on a creative writing scholarship. The Critical Lesson for Parents Don't just let your kids suffer—teach them to struggle well in truth. Many of us developed unhealthy willpower and over-functioned in dysfunctional environments out of fear, not agency. When you teach children that everything they think isn't true and that even when something is true, there's more to the truth God wants them to see, you're giving them the tools for transformation. Come alongside them. Show them how to confess their story to God, ask Him what's true, then walk in that truth. The Three Sources of Thoughts Not every thought you think comes from you. Thoughts come from three sources: (1) your brain's automated patterns, (2) yourself and the Holy Spirit, or (3) the enemy. Learning to discern which source is speaking—and training your children to do the same—is essential for self-brain surgery. Key Takeaways: Start practicing self-brain surgery today. When you're triggered or afraid, confess your actual story to God. Ask Him what's true. Walk in that truth. Let your kids see you do this. Do one hard thing you don't want to do. Your mid-anterior cingulate cortex gets the signal that you're the kind of person who can do hard things, making all future hard things easier. This works for your kids too. Let your children suffer when it's safe to do so. Don't protect them from scraped knees, failed tests, or rejected friendship notes. Their brains are built for this. The Bible promises it. Your child needs evidence that they can survive hard things before they face the next hard thing. Teach the "two truths" practice. When your child says "I'm stupid" or "I'll never learn," acknowledge their feeling ("Yes, this is hard right now") AND teach them to name the truth ("AND you're capable, AND you're learning, AND struggle doesn't define you"). Focus on what you're grateful for, not what scares you. Your hippocampus is a one-way switch—it either activates your fear response or your thinking brain, but not both. Practice gratitude to literally change your brain chemistry and model this for your children. Remember: the generational chaos ends now. God has declared it, and He's made your mind and brain to promise it's true. You can't give what you haven't received, so do this work for your sake AND your children's sake. Closing Thought: "Let your adversity make you more like Christ. It will make you more of who you're supposed to be. The more we stop thinking 'I want to live my own truth and follow my own way' and instead follow His way, the closer we get to Him, the better we use our brains, the better we use our hearts, the more alive we become, the more free we become." - Dr. Lee Warren     Resources: Dr. Lee Warren's new book: The Life-Changing Art of Self-Brain Surgery: Connecting Neuroscience and Faith to Radically Transform Your Life (Available everywhere books are sold, including an audio version read by Dr. Warren) Website: DrLeeWarren.com (for books, podcast, YouTube, Instagram, and the School of Self-Brain Surgery) Dr. Lee Warren's podcast  Connect with Love Is Fearless: Email: janet@john15academy.com Contact information for Formation Cohorts and family consulting. Website: John15Academy.com Together, there is great hope.  

Recorded live at the NTL Summit in Miami, this episode features Cara Rosenthal, co-founder and Chief Legal & Strategy Officer of Expert Radiology, a Puerto Rico–based teleradiology group with a national presence. Cara breaks down their proprietary, patient-first MRI reporting system—featuring colorized key images, side-by-side comparisons, and detailed medical illustrations designed to make injuries instantly understandable. She shares why comprehension leads to better patient compliance, how these reports become powerful built-in demonstratives for injury cases, and what's next as their patent and new SaaS feature expand visual reporting to any radiologist's report.

To have Dr. Morse answer a question, visit: https://drmorses.tv/ask/ All of Dr. Morse's and his son's websites under one roof: https://handcrafted.health/ Facebook Page: https://www.facebook.com/handcrafted.health   00:00:00 - Intro - New Salve! 00:01:17 - SHOC2 Noonan-like syndrome (NS/LAH) - Hypertrophic cardiomyopathy (HCM) 00:38:30 - Multiple Sclerosis (MS) Update - Eyes 00:44:33 - Raynaud's Phenomenon - Interstitial Lung Disease - Scleroderma - Avascular necrosis (AVN) 01:00:15 - Kundalini-like Symptoms - Overstimulated Nervous System - Social Anxiety 01:17:42 - Vitiligo 01:25:26 - Calcium Pyrophosphate Deposition (CPPD) - Osteoarthritis - Knee Surgery 00:01:17 - SHOC2 Noonan-like syndrome (NS/LAH) - Hypertrophic cardiomyopathy (HCM) Can genetic syndromes like my sons be reversed?  00:38:30 - Multiple Sclerosis (MS) - Update - Eyes She was diagnosed with MS via MRI and spinal tap. 00:44:33 - Raynaud's Phenomenon - Interstitial Lung Disease - Scleroderma - Avascular necrosis (AVN) My 17 year old was diagnosed with Raynaud's Phenomenon late 2024. 01:00:15 - Kundalini-like Symptoms - Overstimulated Nervous System - Social Anxiety I am also experiencing fasciculations, buzzing (especially in legs), twitches and tremors. 01:17:42 - Vitiligo I can feel the burning sensation going up to my face and down my left side. 01:25:26 - Calcium Pyrophosphate Deposition (CPPD) - Osteoarthritis - Knee Surgery I want to save his other knee which doctors told him they'd also have to operate on.

When is active surveillance the right choice for intermediate-risk prostate cancer patients? In this episode of BackTable Urology, Dr. Claire de la Calle, Assistant Professor of Urology at the University of Washington, joins Dr. Ruchika Talwar to unpack how active surveillance has evolved beyond low-risk disease and why select Grade Group 2 patients may be appropriate candidates now with thoughtful patient selection. --- SYNPOSIS The conversation explores emerging tools that can refine surveillance decisions, including PSA density, MRI findings, genomic classifiers, and the growing role of AI-assisted pathology. Dr. de la Calle emphasizes the importance of nuanced patient counseling, acknowledging anxiety and long-term risk while reinforcing that time on active surveillance can be a meaningful win when oncologic outcomes remain comparable to upfront treatment. --- TIMESTAMPS 00:00 - Introduction02:58 - Current Evidence05:03 - Patient Selection Criteria12:11 - Importance of PSA Density and Monitoring Protocols18:12 - Pathology and Genomic Testing32:18 - Future Directions and Research36:33 - Key Takeaways --- RESOURCES ProtecT Trial: Fifteen-Year Outcomes after Monitoring, Surgery, or Radiotherapy for Prostate Cancerhttps://www.nejm.org/doi/full/10.1056/NEJMoa2214122 Canary PASS Studyhttps://canarypass.org/ Genomic Classifier Performance in Intermediate-Risk Prostate Cancer: Results From NRG Oncology/RTOG 0126 Randomized Phase 3 Trialhttps://pubmed.ncbi.nlm.nih.gov/37137444

We're back with our monthly rundown of the top headlines in health tech!Today, Halle and Steve sort through the biggest stories shaping the year ahead, from AI prescribing to lawsuits galore.We cover:AI prescribing (in Utah!)The FDA updated guidance on clinical decision support for AI in medicineThe lawsuit against Prenuvo after a missed stroke warning, and the broader debate over accountability in AI-assisted diagnosticsTexas' antitrust case against Epic - are they being anti-competitive?New evidence shows GLP-1 drugs lower employer healthcare costs by 9%Why healthcare hiring is slowing downHalle's book is now available! (Order now on Amazon)Show notes:Utah begins pilot of prescribing AI medication (Utah Department of Commerce)FDA issues guidance on wellness products, clinical decision support software (AHA)Man got $2,500 whole-body MRI that found no problems—then had massive stroke (Ars Technica)Texas sues Epic, accusing it of running a monopoly (Wisconsin Public Radio)Why cover GLP-1s? They'll lower employer healthcare costs, study says (Healthcare Dive)Hospitals' make-or-break year (Axios)

Ozempic is to food chatter as Index cards are to the invisible work of being a household manager. We write any thought down, cognitive offloading, and free up capacity to THINK. As household managers we aren't struggling so much with the housework, it's all of the invisible thoughts that interrupt what we were doing and now can't remember what we need to do. We are constantly volleying between working memory and prospective memory!  Your working memory is your primary executive function. I want your working memory to serve its actual purpose, not just remembering to put the clothes in the dryer. What does that mean? Catch the full episode!!   New Rules Imagine you head back to school after break and the school says that's it, no more backpacks, lockers, or computers. You must carry everything with you throughout the day.  That's a lot to carry right? But we practice this everyday when we try to remember everything with no support staff, no help. And as soon as we think of something we need to do there's a "ding", a notification, a text, an interruption. So just like they gave new rules, I have a new rule for you…write everything down. It's hard to keep trying to remember everything -that's what working memory is - your brain constantly reminding you of what needs to get done. There is science backing the idea that writing things down helps with recall.  One study I shared confirmed yes it's better for recall and another study backed that hypothesis up with an MRI showing different blood flow when we hold a pen and write on paper than even a stylist to a screen.  May I point out that when you are pen to paper there is no notification or anything else interrupting your thought process other than other thoughts. Which if you write each one down they won't interrupt your mindfulness. You can stay focused on your current task. I explained all of this when I gave the example of something as simple as trying to input a passcode. The amount of things that can interrupt you when you are simply "sending yourself a passcode" to then enter on an app, site, or browser that you need, is comical.   I accidentally started using this system, which has proven effective, a long time ago of just writing everything down. And in this fast paced world with notifications distracting you continuously, it's a system to record what you want to remember (Prospective Memory)…what have you got to lose? Go grab a 5 pack of index cards and let your brain's flood gates open, then start writing them down.  Got a Full Classroom? Now imagine that you are the professor. Your working memory (the ability to hold information in your mind and manipulate it to complete work) is the classroom. It's orientation day for over 100 freshman college students. Can you hear all that chatter of the students? Can you even think? All those students are your thoughts. Now, you can clear out that classroom by writing down each thought. You write down the thought, the student leaves the classroom, and you gain back some of your working memory. That's why we write every thought down. You need to quiet your brain so you can think, not remember simple tasks like housework. Just because you are born a girl does not mean you innate know this skill.  Do the System A system works best when you do the system. If you've heard me say it once, then you've heard me say it a thousand times, write down every thought!  Pen to index card. Once you start to cognitively offload your thoughts (to move from your brain to your environment) you free up capacity allowing you to tackle much bigger tasks. Now that you have everything written down, there is no magic that all the sudden everything gets completed. Tune in next week because I am going to tell you the next step and explain why it works. EPISODE RESOURCES: The Sunday Basket® The Productive Home Solution Sign Up for the Organize 365® Newsletter  Did you enjoy this episode? Please leave a rating and review in your favorite podcast app. Share this episode with a friend and be sure to tag Organize 365® when you share on social media