Podcasts about free e

freeeは映像・アニメ・CG制作業界向けの新プロジェクト「freee for 制作」を開始。Autodesk「Flow PT」とのAPI連携により、制作管理ツールと勤怠システムの「二重入力」を解消し、現場の工数データを自動的に経営情報として蓄積。クリエイターの負担軽減と、経営側のリアル…

Link to: Why your guardian angel has been waiting for you to do this one thing.https://youtu.be/JEMAuMGCt4c?si=hUbwxdVuxIvwLBYISpiritual WarfareI'm giving away FREE E-copies of my book. Email me your email address at spiritualwar17@gmail.com for your FREE E-copy. If you have questions or would like us to speak about a certain topic email bradshaw_jc@icloud.comWe will try to answer them as quickly as possible on the podcast.  If you would like to support this Podcast monthly or make a one-time gift you can click the PayPal link below. https://paypal.me/spiritualwar17

Send us Fan MailGrief is something none of us can avoid, yet many of us feel completely unprepared when it touches our lives or the lives of those we love.In this episode of Living the Reclaimed Life, co-host Robin Blumenthal sits down with Ashley Hughes, founder and executive director of Joy in the Mourning. After experiencing the traumatic loss of her husband at a young age, Ashley found herself navigating a journey she never expected. Through her own grief, God planted a passion in her heart to help others who are walking through loss and to equip friends, families, churches, and communities to better support those who are grieving.Ashley shares her personal story, the lessons she has learned through suffering, and practical ways we can come alongside those experiencing loss with compassion, understanding, and hope. This conversation offers encouragement for anyone who is grieving, as well as valuable insight for those who want to be a better friend, family member, or support person to someone facing one of life's most difficult seasons.Sponsor Spotlight: Joy in the MourningThis episode is sponsored by Joy in the Mourning.Joy in the Mourning exists to support those who are walking through grief by equipping individuals and communities with resources, training, and compassionate support so that no one has to navigate grief alone.Be sure to explore their support app and resources designed to help you become the kind of friend that someone who is grieving truly needs.Stay Connected with Ashley: joyinthemourning.orgInstagram: https://www.instagram.com/joyinthemourningnational/Facebook: https://www.facebook.com/joyinthemourningnationalConnect With UsIf this episode encouraged you, please subscribe, leave a review, and share it with a friend who may need hope today.For more resources, stories, articles, podcast episodes, and community, download the Reclaimed Story app and join us as we learn to live the reclaimed life together.Here are two FREE Ebooks for you! 1. Shame Off You: 10 steps to shattering shame in your life, HERE. 2. ABC's: CLICK HERE for a FREE E-book to help you combat lies and replace them with God's truth.  For more encouragement, check out some of our offerings at www.reclaimedstory.comDid you know we have a jewelry line that speaks to your identity in Jesus? CLICK HERE to shop. Every purchase helps support our mission to provide healing and hope to women worldwide. Would you partner with us to spread the message of hope and healing? You can DONATE HERE.  Living the Reclaimed Life is a Reclaimed Story, Inc. podcast, An Arizona non-profit corporation. If you would like to connect with a safe group of women doing real-life together, join our private Facebook page, “Living the Reclaimed Life” or on  Facebook  or Instagram 

Link for NDEhttps://youtu.be/v8EYudRnDqA?si=yg5wosaMxYAApQMISpiritual WarfareI'm giving away FREE E-copies of my book. Email me your email address at spiritualwar17@gmail.com for your FREE E-copy.If you have questions or would like us to speak about a certain topic email bradshaw_jc@icloud.comWe will try to answer them as quickly as possible on the podcast.  If you would like to support this Podcast monthly or make a one-time gift you can click the PayPal link below. https://paypal.me/spiritualwar17

Send us Fan MailCan AI copilots really keep up with pathologists when the cases are new, the workflow is messy, and the benchmark is actually protected from leakage?In this episode of DigiPath Digest #48, I focus on one paper: DALPHIN: Benchmarking Digital Pathology AI Copilots Against Pathologists on an Open Multicentric Dataset. I chose this paper because I think the field needs more of this kind of work. Less hype. More evaluation. Less “look what AI can do.” More “how do we test it in a way that actually means something?” In this session, I look at what makes DALPHIN important for pathologists, lab leaders, and digital pathology trailblazers trying to make sense of pathology AI right now. The paper benchmarks three models against human pathologists: two general-purpose models, Gemini 2.5 Pro and GPT-5, and one pathology-specific model, PathChat+. The dataset includes 1,236 images from 300 cases, covering 130 diagnoses, 14 pathology subspecialties, and cases from six countries. Human performance is benchmarked with 31 pathologists from 10 countries. What I like about this paper is that it does not stop at top-line performance. It deals with the benchmarking problem itself. The authors built a sequestered, indirectly accessible ground truth so the evaluation data could not simply be scraped into model training. That matters because without that protection, benchmarking can become an illusion of genius rather than a real test of generalization. The results are interesting and more nuanced than a simple win-or-lose story. PathChat+ reached expert-level performance in four of six tasks, Gemini in two of six, and GPT in one of six. That tells us something important already: pathology-specific training matters. But it also does not mean pathology is solved. In organ recognition, expert pathologists still outperformed all the models. In rare cancers, none of the models reached expert-level performance. And in ambiguous cases, the models still struggled with something human pathologists do all the time: expressing uncertainty. I also spend time on one of the most practical parts of the paper: model behavior. Gemini tended to overcall. GPT tended to undercall. PathChat was more balanced. That matters in practice. A pathologist using a copilot needs to know the tool's calibration bias before they can safely interpret what it is telling them. I also talk about anchoring bias in conversational interfaces, where early hallucinations can propagate through later answers if memory is not reset between questions. That is not just a technical curiosity. That is a workflow and safety issue. Why should you listen? Because this episode is really about a bigger question: What kind of evidence should pathologists demand before AI copilots enter real workflows? If you want to understand validation, data leakage, rare-case performance, uncertainty, and why these tools should still be treated as co-pilots rather than autopilots, this is a useful paper to know. Episode Highlights01:20 – Why I chose the DALPHIN preprint and why benchmarking matters right now. 05:38 – What is in the DALPHIN dataset: 300 cases, 130 diagnoses, 14 subspecialties, 6 countries. 07:57 – Top-line performance: PathChat+ reaches expert-level performance in 4 of 6 tasks. 09:41 – The benchmarking trap of data leakage and why DALPHIN's sequestered ground truth matters. 12:19 – Why real pathology diagnosis is not text-only and why macro + micro context matters. 15:26 – Tissue recognition, neoplasm detection, ambiguity, and conversational memory: how the testing was structured. 21:29 – The diagnostic personalities of the models: overcalling, undercalling, and balanced behavior. 24:36 – Rare cancers: where AI copilots still fall short of expert human performance. 28:00 – Why binary outputs are not enough when pathology often lives in uncertainty. 31:37 – Anchoring bias and conversational memory: how early hallucinations can keep propagating. 37:11 – Why these tools should be treated as co-pilots, not autopilots. 40:29 – Resources for beginners: Digital Pathology 101 and continued AI literacy. Resources mentionedDALPHIN preprint: arXiv:2605.03544v1 DALPHIN evaluation platform: dalphin.grand-challenge.org PathChat+ pathology-specific AI model discussed in the benchmark. Digital Pathology 101 free eBook by Dr. Aleksandra Zuraw. Educational streams on tissue recognition and computer vision literacy mentioned in the session.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

Wasatch Front residents can apply starting today for up to $800 toward an e-bike — we break down who qualifies and how to get yours before the 2,000 vouchers run out. Plus, the latest on the Tyler Robinson hearing, why Utah's odd spring could mean a "fruit famine," and Jell-O is getting a healthier makeover (just in time for National Candy Month).   We also dig into Malaysia banning social media for kids under 16, a growing form of bullying called lunch shaming, real estate agents leaving the industry in droves, and how remote work is making it harder for young people to find jobs. Amy Donaldson with KSL Podcasts joins us in-studio to preview her Coach's Book Club interview with new Utah Football head coach Morgan Scalley. And we close out with the AI question of the day and Sacramento's big MLB expansion reveal. How does Sacramento compete with Utah?    Follow KSL Brightside on social media! YouTube: https://www.youtube.com/@KSLBrightside Facebook: https://www.facebook.com/KSLBrightside Instagram: https://www.instagram.com/KSL_Brightside TikTok: https://www.tiktok.com/@ksl.brightside

Spiritual WarfareI'm giving away FREE E-copies of my book. Email me your email address at spiritualwar17@gmail.com for your FREE E-copy. If you have questions or would like us to speak about a certain topic email bradshaw_jc@icloud.comWe will try to answer them as quickly as possible on the podcast.  If you would like to support this Podcast monthly or make a one-time gift you can click the PayPal link below. https://paypal.me/spiritualwar17

Send us Fan MailWhat if caring for your health could become an act of worship instead of pressure or perfectionism?In this episode of Living the Reclaimed Life, Denisha sits down with Christian health advocate Nelly Holst for a deeply encouraging conversation about faith, nutrition, healing, and learning to steward the body God has given us.Nelly shares her powerful story of walking through decades of chronic illness, discovering biblical principles for healthy living, homeschooling her three daughters for 17 years while battling health challenges, and ultimately learning how deeply God cares for every part of us, body, soul, and spirit.Together, they discuss:• Nelly's journey to faith and the moment Jesus became real to her• Living with celiac disease before most doctors understood it• What Scripture says about caring for our bodies• Simple practical changes women can make toward better health• Why stewardship matters more than perfection• The connection between physical health, emotional health, and spiritual health• Non-invasive light therapy and holistic wellness• Encouragement for exhausted women who have spent years caring for everyone elseThis conversation is filled with wisdom, grace, hope, and practical encouragement for women who want to live healthier lives while staying rooted in Christ.If this episode encouraged you, be sure to subscribe, leave a review, and share it with a friend who needs hope today.Stay connected with Nelly Holst: Email: healthycoachingtoday@gmail.comhttps://linktr.ee/healthylivinghappenstodayFacebookInstagramLinkedInHere are two FREE Ebooks for you! 1. Shame Off You: 10 steps to shattering shame in your life, HERE. 2. ABC's: CLICK HERE for a FREE E-book to help you combat lies and replace them with God's truth.  For more encouragement, check out some of our offerings at www.reclaimedstory.comDid you know we have a jewelry line that speaks to your identity in Jesus? CLICK HERE to shop. Every purchase helps support our mission to provide healing and hope to women worldwide. Would you partner with us to spread the message of hope and healing? You can DONATE HERE.  Living the Reclaimed Life is a Reclaimed Story, Inc. podcast, An Arizona non-profit corporation. If you would like to connect with a safe group of women doing real-life together, join our private Facebook page, “Living the Reclaimed Life” or on  Facebook  or Instagram 

Graduating on an F1 visa and thinking about starting your own business in the U.S.? The E2 investor visa might be your best path forward and if you're currently on OPT, you may be able to use that time to test and launch your business before you ever file. In this video, immigration attorney John Khosravi breaks down everything F1 students need to know about the E2 visa: who qualifies (and which countries are excluded), how much you need to invest, what your business plan must include, and how to document your source of funds correctly from day one.

Heath McWilliams

If you are the adult child of a U.S. citizen or green card holder, getting married or divorced at the wrong time could seriously affect your immigration case. In this video, we break down a real immigration story of a green card holder who almost lost their path to U.S. citizenship because of a complicated family-based immigration history involving the F2B, F1, and F3 visa categories. Learn how marriage, divorce, a parent's naturalization, and timing can impact your green card process, citizenship eligibility, and even put your status at risk. If you are waiting for a family-based green card, this is important information you should know.

Spiritual WarfareI'm giving away FREE E-copies of my book. Email me your email address at spiritualwar17@gmail.com for your FREE E-copy. If you have questions or would like us to speak about a certain topic email bradshaw_jc@icloud.comWe will try to answer them as quickly as possible on the podcast.  If you would like to support this Podcast monthly or make a one-time gift you can click the PayPal link below. https://paypal.me/spiritualwar17

Send us Fan MailDo you really need a scanner, whole slide images, and AI infrastructure before you can start in digital pathology?In this episode, I argue that you do not.I'm Dr. Aleksandra Zuraw, veterinary pathologist and digital pathology educator, and this talk is about a belief I hear all the time: I don't have the tools yet, so there is no point learning digital pathology. I used to think that too. When I was training in Berlin, there was one Leica 6-slide scanner, and it felt like digital pathology was only for a small group of chosen people. That experience made the field feel distant, exclusive, and not really available to beginners. What changed for me was not a new scanner. It was a small project.I needed a more consistent way to quantify a senescence marker in archived skin samples, so I used a microscope camera, captured images, opened them in Microsoft Paint, and manually marked cells with colored dots. It was scrappy. Very low tech. But it was also digital, consistent, and verifiable. That project became my first real step into digital pathology and helped me get my first job in the field, where I worked between pathologists and image analysis scientists on biomarker quantification and patient stratification problems. That is the core point of this episode: knowledge unlocks technology.Scanners matter. AI tools matter. But the deeper bottleneck is whether enough people understand how to use these tools, ask good questions, and connect pathology expertise with digital workflows. That is why this episode is really about readiness. Not readiness of the hardware. Readiness of the people.I also talk about Dr. Taladzer from Pakistan, whose story makes this point even more clearly. At the time, Pakistan had around 220 million people, about 500 pathologists, and zero scanners. She still started learning digital pathology during COVID using a microscope and camera, joined the Digital Pathology Association, taught herself from papers and online resources, and kept going even after multiple AI vendors rejected her because she did not have whole slide images. Eventually, she found a DIY image analysis platform, learned to annotate and train models on static images, completed projects quickly, and went on to publish more than 10 digital pathology papers without ever using WSI.Why should you listen?Because this episode is for pathologists and lab leaders who are interested in digital pathology but still feel stuck at the beginning. It is for people waiting for permission, perfect infrastructure, or a formal roadmap. And it is for trailblazers who came back from a meeting or conference energized, but need a practical way to turn that energy into action before it fades.I also address an important AI question near the end: How do we know an AI model is good enough for pathology? I talk about why models are only as good as the pathologist annotations used to train them, why concordance between pathologists matters, how orthogonal labels like IHC can improve model quality, and why pathologists still need to stay in the loop as these systems develop and get deployed.If you are trying to figure out where to start, this episode gives you a practical answer: start where you are. Start with what you have. Start learning now.Episode Highlights00:00 – Why the real barrier to digital pathology is usually not the hardware 00:33 – What it feels like to be at the beginning of the digital pathology journey 02:50 – My first practical digital pathology project using a microscope camera and Microsoft Paint 05:37 – How that low-tech project led to my first digital pathology job 08:52 – Why knowledge, not infrastructure, is the real unlock 09:57 – Dr. Taladzer's story: starting digital pathology in Pakistan with zero scanners 12:03 – What happened after repeated vendor rejection and why persistence mattered 14:39 – The “forgetting loop” vs the “commitment loop” after conferences 16:48 – Practical next steps: book, PubMed alerts, journal clubs, webinars, vendor resources 18:52 – Why I believe digital pathology is the gateway to faster diagnosis 20:00 – How to think about whether an AI model is really ready for pathologyResources MentionedDigital Pathology 101 – free book recommended as a starting point for learning digital pathology. Digital Pathology Association – mentioned as a learning resource and professional community. PubMed alerts for AI and digital pathology. Journal clubs – mentioned as one way to keep learning consistently. Webinars and vendor resources – suggested as practical ways to keep building knowledge. A4A – the DIY image analysis platform that supported Dr. Taladzer's early work with static image annotation and model training. Support the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailWhy are pathology vendors still speaking different image languages when radiology solved that problem decades ago?In this episode of DigiPath Digest #46, I talk through four papers that all point to a bigger issue in digital pathology: we are not only dealing with better algorithms. We are dealing with interoperability, workflow design, explainability, and whether the field is actually ready to use these tools well.I start with DICOM in digital pathology, because I think this is still one of the most important infrastructure questions in the field. Digital pathology has clear value for consultation, image analysis, archival, and workflow, but vendor-specific whole slide image formats still create silos. In the episode, I explain why DICOM matters, why adoption is still low, how the multi-resolution pyramid works, and why this is really about enterprise imaging and future-proofing, not just file conversion. Then I move into kidney transplant rejection, where the paper makes a strong case for multimodal precision diagnostics. Creatinine is late. Antibody testing can miss important biology. Biopsies can miss the area that matters. So the opportunity is not to replace pathology, but to combine biomarkers, biopsy, and machine learning in a way that is more useful than any one signal alone. I also talk about explainability here, because if a model gives a risk score, we need to know what contributed to it. The third paper focuses on perineural invasion in solid tumors, and I liked this one a lot because it shows how AI can help standardize something that is clinically important but still inconsistently detected and reported. Perineural invasion is not just a passive pathway of spread. The biology is more active than that, and the quantification can go far beyond a simple yes-or-no answer. This is a good example of where digital pathology can do something humans cannot realistically do by eye at scale. The last paper is on gastric cancer immunohistochemistry biomarkers and advanced quantification, including HER2, PD-L1, mismatch repair, and CLDN18.2. This section is really about complexity. We are now asking pathologists to visually score biology that is getting harder and harder to summarize consistently, especially when markers, spatial context, and multiplexing all start to matter at once. I make the case that computational pathology is becoming necessary here, not because pathologists are failing, but because the biology is outgrowing purely visual workflows. What ties these four papers together is simple: digital pathology is not only about remote reading anymore. It is about interoperability, quantification, explainable AI, and making pathology more precise in places where the old workflow is reaching its limit. If you are a pathologist, lab leader, or digital pathology trailblazer trying to figure out what actually matters right now, this episode will help you connect the dots.Episode Highlights 07:41 – Why DICOM still matters if we want digital pathology systems to work together. 14:39 – Current adoption of SVS, MRXS, and DICOM, and why DICOM is still lagging. 16:44 – How the DICOM whole slide image pyramid works and why it matters for workflow. 24:29 – Why kidney transplant rejection is still difficult to diagnose with any single marker. 29:18 – Why perineural invasion is clinically important and still inconsistently reported. 34:44 – How AI can quantify tumor-nerve relationships more consistently than visual review alone. 46:39 – Why gastric cancer biomarker scoring is getting too complex for purely visual workflows. 54:55 – Multiplexing, spatial biology, and why explainable AI matters in biomarker interpretation. 01:04:01 – What is really blocking digital pathology adoption: cost, workflow, regulation, or mindset? Resources mentionedDICOM / digital pathology interoperability paper https://pubmed.ncbi.nlm.nih.gov/42093730/Kidney transplant rejection, biomarkers, and artificial intelligence https://pubmed.ncbi.nlm.nih.gov/42073482/Perineural invasion in solid tumors with AI and machine learning applications https://pubmed.ncbi.nlm.nih.gov/42100436/Gastric cancer IHC biomarkers, advanced detection methods, and perspectives https://pubmed.ncbi.nlm.nih.gov/42075555/Digital Pathology Place https://digitalpathologyplace.comDigital Pathology 101 Free PDF book mentioned at the end of the episode through Digital Pathology Place.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailWhat if the most frightening part of a pathology report is not the word cancer, but the silence that follows?In this episode of the Digital Pathology Podcast, Dr. Aleksandra Zuraw talks with Michele Mitchell—breast cancer survivor, caregiver, national patient advocate, and longtime volunteer across Michigan Medicine, ASCP, the Digital Pathology Association, and MyPathologyReport.ca—about what happened when she saw her own cancer slide years after treatment. That moment changed how she understood her disease, her risk, and her role as a patient advocate.This is not just a patient story. It is a digital pathology implementation story.The episode looks at how digital pathology removes practical barriers to sharing slides, why pathology clinics matter, and what becomes possible when pathologists move from being hidden in the background to becoming direct contributors to patient understanding. Michelle and Dr. Aleks talk through the communication gap around pathology reports, the emotional cost of delayed explanation, and the real-world workflow of pathology clinic visits built to help patients review their slides with the pathologist who made the diagnosis.They also discuss what the 21st Century Cures Act changed for patients, why immediate access to reports without interpretation can still create fear, and how pathology clinics can bridge the gap between raw data and real understanding. The conversation gets practical too: how patients can request a pathology clinic visit, what virtual pathology consults can look like, how billing and workflow concerns are already being addressed, and why the infrastructure question is smaller than many people assume.If you work in digital pathology, pathology informatics, patient communication, or implementation, this episode is a reminder that visibility is not extra. It is part of the value proposition. And for pathologists who worry this is too far outside the traditional role, the episode offers a grounded counterpoint: the workflows, templates, billing structures, and virtual options already exist.Highlights00:00 – Why pathology needs to become more patient-centered Michele frames the core problem clearly: what often scares patients is not only cancer, but the silence around the diagnosis. 00:34 – How digital pathology changes the patient experience Digital slides make it possible for patients to see their diagnosis, compare normal and abnormal tissue, and ask better questions. 11:13 – What happened when Michele saw her cancer for the first time More than a decade after treatment, seeing her own slide changed how she understood her grade, her risk, and her daily health decisions. 16:19 – Why visual pathology can change adherence and lifestyle Michele explains how the image-based explanation became a practical turning point, not just an emotional one. 20:43 – The case for direct pathologist-patient communication The episode reviews why this can improve clarity, treatment understanding, clinic efficiency, and even professional satisfaction for pathologists. 38:40 – What a pathology clinic actually looks like From preparation and consent to slide review, plain language, empathy, and follow-up, the workflow is much more concrete than many people assume. 45:35 – ASCP's certification workshop for pathology clinics Michele describes the national effort to make pathology clinics reproducible, scalable, and easier to implement. 49:32 – What the 21st Century Cures Act changed Patients now get near real-time access to reports, but that access still needs interpretation, context, and support. 01:03:23 – Pushback, logistics, and why the barriers are not where people think Time, reimbursement, scheduling, and virtual setup are addressed directly with examples already in practice. 01:16:57 – The future: patient-friendly reports, AI, and pathology as part of the care team The episode closes on a practical vision: not hype, but tools and workflows that already exist and can be connected now. Resources mentionedDigital Pathology Place – website and educational platform referenced by Dr. Aleks as the home for her work and resources. Digital Pathology 101 – Dr. Aleks's book, referenced in the broader discussion of patient and pathologist education. Michigan Medicine breast pathology clinic – launched in 2023 as a patient-facing breast pathology clinic model. ASCP pathology clinic certification workshop – national workshop co-developed to help institutions build pathology clinics. 21st Century Cures Act – legal framework behind near real-time patient access to pathology reports and related health data. MyPathologyReport.ca – patient-friendly pathology education resource reviewed with patient advocate involvement. American Cancer Society Reach to Recovery – support resource mentioned for breast cancer patients. Scanslated – patient-friendly report interface discussed as part of a future-facing model for pathology communication. Virtual pathology consults/telehealth setup – discussed as a scalable way to lower implementation friction.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

If you have a pending I-485 employment-based green card case, traveling outside the U.S. can carry serious risks if not handled correctly. In this video, immigration attorney John Khosravi from JQK Immigration Law Firm explains what H-1B, H-4, L-1, and L-2 visa holders must understand before leaving the country, how Advance Parole (Form I-131) works, and why timing and strategy are critical to avoid abandoning your green card case or losing your visa status. You'll also learn about expedite options, emergency travel requests, and how employment-based families can protect their case while planning necessary travel.

Spiritual Warfare I'm giving away FREE E-copies of my book. Email me your email address at spiritualwar17@gmail.com for your FREE E-copy. If you have questions or would like us to speak about a certain topic email bradshaw_jc@icloud.comWe will try to answer them as quickly as possible on the podcast.  If you would like to support this Podcast monthly or make a one-time gift you can click the PayPal link below. https://paypal.me/spiritualwar17

Send us Fan MailDigiPath Digest #45 asks a practical question: can AI in pathology move from correlation to real clinical use? In this episode, I review four papers that push on that question from different angles: computational pathology moving toward morphology-driven molecular inference, the current state of digital cytopathology and AI, multi-omics and precision oncology in hepatocellular carcinoma, and AI literacy in veterinary education. What ties them together is not model performance alone. It is the harder question of validation, workflow fit, quantitative use, ethics, and human oversight.In the first paper, I talk about computational pathology as more than pattern recognition. The focus is on morphology-driven molecular inference, digital biomarkers, and why spatial omics matters as biological ground truth. I also discuss why continuous quantitative scoring is more useful than forcing biology into rough scoring buckets. The second paper focuses on digital cytopathology. Cytology was early for FDA-cleared AI in cervical screening, but non-gynecologic cytology is still much harder to digitize because of specimen variability and workflow complexity. I also cover telecytology, rapid onsite evaluation, automation, and quality control. The third paper looks at hepatocellular carcinoma and AI-driven precision oncology. This part is about using AI and machine learning to integrate genomics, transcriptomics, proteomics, metabolomics, radiomics, and pathology to support biomarker discovery, tumor microenvironment analysis, and treatment stratification. The fourth paper may be the most broadly useful. It proposes an AI literacy curriculum for veterinary education that covers AI fundamentals, machine learning evaluation, LLMs, ethics, liability, and academic integrity. I think that matters far beyond veterinary medicine, because if clinicians are expected to use AI tools responsibly, AI literacy cannot stay optional. Highlights 00:01 Welcome and overview of the four papers 03:02 Computational pathology and morphology-driven molecular inference 11:01 Digital cytopathology, telecytology, and QC 20:47 AI/ML in hepatocellular carcinoma precision oncology 31:04 AI literacy in veterinary education 47:42 Final takeaways and Digital Pathology 101 update ResourcesComputational Pathology as a Mechanistic Discipline: From Morphology to Molecular Data https://pubmed.ncbi.nlm.nih.gov/42052846/Advances in Digital Cytopathology and Artificial Intelligence Applications https://pubmed.ncbi.nlm.nih.gov/42046894/Navigating the Labyrinth of Hepatocellular Carcinoma: Leveraging AI/ML for Precision Oncology https://pubmed.ncbi.nlm.nih.gov/42065059/Curriculum Framework for Artificial Intelligence Literacy in Veterinary Education Front Vet Sci. 2026;13:1801756 Support the showGet the "Digital Pathology 101" FREE E-book and join us!

If you have an approved I-140 and are pursuing your employment-based green card (EB-1, EB-2, or EB-3) through consular processing at a U.S. embassy, there are critical steps and pitfalls you need to know before moving forward. Immigration Attorney John Khosravi of JQK Law breaks down the National Visa Center process, why the Affidavit of Support is NOT required for most employment-based cases, how to properly complete the DS-260, why mailing documents is still required as of April 2026, how to protect aging-out children under CSPA, and what to expect after your embassy interview including administrative processing, the DS-5535, and current travel ban risks. Prepare smarter and avoid costly delays. 

If you entered the U.S. on a J-1 visa and are now married to a U.S. citizen, this video breaks down the biggest obstacle you may face the 2-year home residency requirement and how it affects your green card process. Immigration attorney John Khosravi explains your two main options (adjustment of status vs. consular processing), the risks of falling out of status, current 2026 updates on J-1 waivers, and how travel bans and delays can impact your case. This is a must-watch if you're unsure whether you can stay in the U.S. or need to leave to continue your application. For a deeper step-by-step guide, visit marriageimmigrationlaw.com or schedule a consultation to review your specific situation.

Military spouse citizenship can be faster than you think. In this video, immigration attorney John Khosravi explains how green card holders married to active-duty service members may qualify for expedited U.S. citizenship, potentially skipping the usual 3- or 5-year wait. Learn the key rules, timelines, and requirements for military spouse naturalization, including what to do before deployment overseas, how to handle long absences from the U.S., and when to apply for forms like N-400 or re-entry permits. If you're a military spouse planning to leave the country, this guide will help you avoid delays and take advantage of special USCIS exceptions.

Spiritual WarfareI'm giving away FREE E-copies of my book. Email me your email address at spiritualwar17@gmail.com for your FREE E-copy. If you have questions or would like us to speak about a certain topic email bradshaw_jc@icloud.comWe will try to answer them as quickly as possible on the podcast.  If you would like to support this Podcast monthly or make a one-time gift you can click the PayPal link below. https://paypal.me/spiritualwar17

Send us Fan MailWhy run from love? The love of God is so powerful that He meets us in our most painful places. Jasmine Esquivel is a pastor, teacher, and healing minister based in Tucson, Arizona, where she serves at Iglesia Centro de Sanidad. Having experienced both deep disappointment and profound restoration, she carries a passion to help people encounter the love of the Father. Jasmine shares her personal story that is real, raw and deeply vulnerable. In this conversation, you'll hear struggle, tears, surrender and the healing love of God, meeting someone in the middle of deep pain. As you listen, you may hear parts of your own story reflected in hers. Please note this episode is not intended for children, as we do discuss real-life topics, including pregnancy complications and abuse within the church. Stay connected with Jasmine: WebsiteInstagramFacebookFor more informationWe want to thank the sponsors for today's episode, Joy in the Mourning. Joy in the morning exists to support those who are walking through grief by equipping individuals and communities with resources, training, and compassionate support so that no one has to navigate grief alone. Be sure to check out their incredible support app, designed to help you become the kind of friend that someone who is grieving is truly in need of.  You can connect with them at https://www.joyinthemourning.org/Here are two FREE Ebooks for you! 1. Shame Off You: 10 steps to shattering shame in your life, HERE. 2. ABC's: CLICK HERE for a FREE E-book to help you combat lies and replace them with God's truth.  For more encouragement, check out some of our offerings at www.reclaimedstory.comDid you know we have a jewelry line that speaks to your identity in Jesus? CLICK HERE to shop. Every purchase helps support our mission to provide healing and hope to women worldwide. Would you partner with us to spread the message of hope and healing? You can DONATE HERE.  Living the Reclaimed Life is a Reclaimed Story, Inc. podcast, An Arizona non-profit corporation. If you would like to connect with a safe group of women doing real-life together, join our private Facebook page, “Living the Reclaimed Life” or on  Facebook  or Instagram 

Send us Fan MailWhere is AI in pathology actually becoming useful right now? In this episode of DigiPath Digest, I review 4 new PubMed papers across digital pathology, whole slide imaging (WSI), computational pathology, medical education, forensic pathology, and breast cancer AI. We look at a deep learning tool for coronary artery stenosis measurement in forensic autopsies, an AI-powered digital pathology model for renal pathology education, an open-source quality control tool for prostate biopsy whole slide images, and a breast cancer stage prediction model built for resource-constrained settings using low-magnification H&E slides. I also share updates on the upcoming second edition of Digital Pathology 101 and the decision to make AI paper summaries public on the podcast feed to help busy pathology professionals stay current. Highlights  [01:28] Update on the upcoming second edition of Digital Pathology 101 and the release of public AI paper summaries for faster literature review. [05:22] Paper 1: Deep learning for coronary artery stenosis evaluation in forensic autopsies using whole slide imaging. Why objective stenosis measurement matters, how the model outperformed visual estimates, and why this could affect adoption in forensic pathology. [15:18] Paper 2: AI-powered digital pathology with case-based teaching in renal education. A practical discussion on annotated digital slides, flipped classroom learning, and how digital pathology can improve pathology education and diagnostic reasoning. [21:34] Paper 3: Open-source AI for quantitative quality control in prostate biopsy whole slide images. Why WSI quality control matters, what PathProfiler measures, and how automated QC can support remote pathology workflows. [32:38] Paper 4: Breast cancer stage prediction from H&E whole slide images in resource-constrained settings. A look at low-magnification AI, vision transformers, and what moderate performance can still mean when access to advanced testing is limited. [45:06] Closing thoughts, invitation to vote for future AI paper summaries, and a final reminder to download Digital Pathology 101. Resources Paper 1: Development of a deep learning-based tool for coronary artery stenosis evaluation in forensic autopsies using whole slide imaging PubMed: https://pubmed.ncbi.nlm.nih.gov/41998396/Paper 2: Integrating AI-Powered Digital Pathology With Case-Based Teaching: A Novel Paradigm for Renal Education in Medical School PubMed: https://pubmed.ncbi.nlm.nih.gov/41995002/Paper 3: Application of an open-source AI tool for quantitative quality control in whole slide images of prostate needle core biopsies - a retrospective study PubMed: https://pubmed.ncbi.nlm.nih.gov/41994924/Paper 4: Deep-learning-based breast cancer stage prediction from H&E-stained whole-slide images in resource-constrained settings PubMed: https://pubmed.ncbi.nlm.nih.gov/41993946/Support the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailPaper Discussed in this Episode:Deep-learning-based breast cancer stage prediction from H&E-stained whole-slide images in resource-constrained settings. Bedőházi Z, Biricz A, Kilim O, et al. Journal of Pathology Informatics 21 (2026) 100644.Episode Summary:Welcome back, Trailblazers! In this Journal Club deep dive of the Digital Pathology Podcast, we flip the core assumption of microscopic precision on its head. Can an AI accurately predict pathological breast cancer stages (pTNM I-III) from a blurry, high-altitude 2.5x magnification snapshot? We explore a 2026 study that strips away standard high-resolution data to build a highly efficient, resource-aware AI diagnostic tool for clinics lacking supercomputers. We unpack the math, the models, and a haunting revelation about what primary tumors can tell us about distant metastasis.In This Episode, We Cover:• The Compute Bottleneck: Why the digital pathology AI revolution is leaving resource-constrained clinics behind, and how dropping from the standard 40x to 2.5x magnification slashes image patch extraction by 256 times, bypassing massive hardware and server requirements.• The "Airplane View": How the AI compensates for the loss of microscopic cellular details (like mitosis or cellular atypia) by relying on macroscopic features, identifying disease through overall tumor growth patterns and broad architectural disruption.• Vision Transformers & "Puzzle Bags": Why the UNI foundation model—a vision transformer fine-tuned on the BRACS dataset—outperforms older convolutional networks (like ResNet-50) by mapping long-range spatial dependencies across the entire image patch simultaneously. Plus, how Multiple Instance Learning (MIL) acts as a targeted "puzzle bag," mathematically weighting critical cancer data and ignoring irrelevant background noise.• The Real-World Stress Test: The model's solid performance on the internal Semmelweis dataset versus the massive external Nightingale cohort, where unsupervised data cleaning with t-SNE and DBSCAN clustering automatically deleted garbage data. We also discuss the AI's struggle with the TCGA-BRCA dataset due to severe domain shift from heterogeneous tissue preparation, specifically the structural tissue damage caused by frozen sections.• The "Messy Middle" and Clinical Triage: The model's tendency to struggle with Stage II breast cancer and the critical clinical danger of under-staging advanced Stage III cancers. We discuss why this WSI-only baseline isn't replacing human pathologists, but rather serves as an automated "sorting hat" for incomplete medical records or a highly tunable "smoke detector" to route suspicious slides for immediate manual review.Key Takeaway:The AI successfully predicted overall cancer stage—which inherently includes distant lymph node metastasis—by looking only at the primary tumor's architectural disruption, without ever evaluating a single lymph node slide. This proves that vital systemic biological secrets are hiding in plain sight in the macroscopic view of standard H&E slides, offering a phenomenal proof-of-concept for global health equity in resource-constrained settingsSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

Spiritual WarfareI'm giving away FREE E-copies of my book. Email me your email address at spiritualwar17@gmail.com for your FREE E-copy. If you have questions or would like us to speak about a certain topic email bradshaw_jc@icloud.comWe will try to answer them as quickly as possible on the podcast.  If you would like to support this Podcast monthly or make a one-time gift you can click the PayPal link below. https://paypal.me/spiritualwar17 

Send us Fan MailPaper Discussed in this Episode:Integrating AI-Powered Digital Pathology With Case-Based Teaching: A Novel Paradigm for Renal Education in Medical School. Zhou H, Cui L. Clin Teach 2026; 23(3):e70421. doi: 10.1111/tct.70421.Episode Summary: In this journal club episode tailored for healthcare trailblazers, we explore a massive paradigm shift in medical education. We examine a 2026 perspective article that uses the notoriously complex field of renal pathology as a stress test for a brand-new teaching model. Moving away from dark lecture halls and static, perfect images, we discuss what happens when artificial intelligence is actively combined with flipped classrooms, fundamentally redefining what it means to be a competent physician in the digital age.In This Episode, We Cover:• The "Bottleneck" of Renal Pathology: Why the kidney is the ultimate teaching hurdle. Students must translate the dense, flattened 2D reality of an H&E stain into an understanding of a patient's complex systemic autoimmune response.• The Danger of the "Curated Reality": Why traditional teaching methods that rely on textbook-perfect, heavily curated slides create "brittle" mental models. When students finally encounter messy, real-world biopsies with overlapping, ambiguous pathologies, the traditional educational foundation falls apart.• The "Spell Checker" for Histopathology: How collaborative AI elevates Whole Slide Imaging (WSI) beyond just high-resolution screens. The AI acts as a concurrent guide, using pixel-level pattern recognition to highlight regions of interest simultaneously and simulate the complex reasoning process of an expert pathologist.• The Case-Based Flipped Classroom (CBFC): The pedagogical engine that anchors these AI tools in clinical reality. Instead of passive lectures, students are handed the "detective's case file" beforehand to actively interrogate annotated slides, synthesizing diverse data streams to defend diagnoses in collaborative groups.• Redefining Medical Competence (The "Clinical Editor"): Why the new bottleneck in medical education isn't memorization—it's critical appraisal. We discuss the necessity of teaching "digital literacy," training students to skeptically manage AI, recognize its blind spots (like confusing a physical tissue fold for an abnormality), and actively audit the algorithm against the messy human reality of the patient.• The Impending Culture Collision: A look at the fascinating future where freshly minted, AI-native residents enter a legacy clinical workforce still transitioning away from physical glass slides, potentially reversing traditional medical hierarchies in the hospital.Key Takeaway: The goal of modern medical education is no longer just memorizing histological patterns, as that heavy lifting is being outsourced to algorithms. By fusing AI-powered digital pathology with the necessary friction of case-based learning, we are training a new generation of diagnosticians to view AI not as a crutch, but as a powerful collaborative tool that must be thoughtfully scrutinized and audited for safe patient careSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailPaper Discussed in this Episode: Advancements in bone marrow biopsy: the role of omics and artificial intelligence in hematologic diagnostics. Maryam Alwahaibi and Nasar Alwahaibi. Front. Med. 2026; 13:1772478.Episode Summary: In this journal club deep dive, we explore a paradigm shift in hematopathology, moving from 19th-century visual assessments to the cutting edge of precision medicine. We examine a 2026 review that unpacks how combining artificial intelligence with multi-omics technologies is transforming the traditional bone marrow biopsy from a static, subjective snapshot into a live, interactive, predictive 3D map. We ask: What happens when deep learning can predict underlying genetic mutations just by analyzing the visual shape and texture of a cell?.In This Episode, We Cover:The Breaking Point of Traditional Diagnostics: Why the 150-year-old gold standard of H&E staining and human visual assessment is hitting a biological and operational wall, plagued by subjectivity, high variability, and observer fatigue.The Multi-Omics Multiverse: Moving beyond standard genomics to unpack the complex biological machinery of the marrow, including:Epigenomics: The biological "switches," like DNA methylation, that control cell fate and can kick off malignant transformation without altering the underlying DNA sequence.Lipidomics: How cellular fats form specialized signaling rafts that actively remodel the marrow's communication network.Microbiomics (The Gut-Marrow Axis): How systemic inflammation driven by gut dysbiosis acts like a massive "traffic jam" that indirectly disrupts local bone marrow homeostasis and blood cell production.AI as the Ultimate Analytical Partner: How artificial intelligence serves as a bridge between physical tissue morphology and high-dimensional molecular data. We discuss AI tools like MarrowQuant for objective cellularity mapping and the Continuous Index of Fibrosis (CIF) that replaces clunky human guesswork with a granular, predictive metric.Predicting Genotype from Phenotype: The revolutionary capability of deep learning models to predict underlying genetic mutations (like TET2 or del 5q MDS) purely from the subvisual, spatial arrangement and shape of cells on a standard slide.Roadblocks and Solutions: Why this technology isn't universally adopted yet. We break down the "black box" problem of AI, the brittleness of algorithms in different clinical settings, and how innovations like Federated Learning and Explainable AI (using heat maps) are overcoming these hurdles.Key Takeaway: The integration of AI and multi-omics is redefining our understanding of bone marrow diseases. By uncovering invisible molecular machinery and objectively translating it through transparent algorithms, we are moving away from subjective human bottlenecks toward a highly personalized, predictive model of hematologic care.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailPaper Discussed in this Episode: Artificial intelligence in clinical oncology: Multimodal integration and translational development. Ruichong Lin, Zhenhui Zhao, Zhonghai Liu, Jin Kang, Kang Zhang, Xiaoying Huang, Yunfang Yu. Cancer Letters 2026; Volume 649, 218493.Episode Summary: In this journal club deep dive, we explore how cutting-edge AI is fundamentally rewriting the rules of cancer diagnostics. We examine a comprehensive 2026 review on clinical oncology that highlights the shift from narrow, single-modality algorithms to highly sophisticated multimodal AI. We discuss how machines are learning to cross-reference patient charts, genomic data, and medical imaging simultaneously to achieve unprecedented feats—like accurately predicting tumor mutations without ever performing a physical biopsy. Plus, we explore the controversial but necessary world of "computational hallucinations" or synthetic data, which is currently being used to solve diagnostic blind spots.In This Episode, We Cover:• The Fragmentation Bottleneck: Why keeping radiology, pathology, genomics, and clinical history in isolated silos limits our ability to treat the whole patient, and why single-modality AI suffers from severe diagnostic "tunnel vision".• Cross-Modal Attention & Non-Invasive Biopsies: How models like LUCID essentially mimic the deductive reasoning of a multidisciplinary tumor board. By utilizing cross-modal attention mechanisms, LUCID dynamically shifts focus between CT scans, routine labs, and text-based clinical charts to predict EGFR gene mutations in lung cancer entirely non-invasively.• Graph Neural Networks (GNNs) & Tumor Social Networks: A look at the NePSTA framework, which uses GNNs and spatial transcriptomics to treat the tumor microenvironment like a mathematical topology. By mapping the "social network" of cells, it can rapidly molecularly subtype notoriously ambiguous central nervous system (CNS) tumors in minutes.• Computational Hallucinations: Introducing MINIM, a generative AI foundation model that creates statistically valid, photorealistic synthetic medical images (like optical CT or chest X-rays) for rare diseases based on textual descriptions. We discuss how intentionally generating these synthesized images solves the critical "data scarcity" problem and directly improves real-world diagnostic accuracy.• The Reality Check - Distribution Shifts: The dangerous logistical reason why an AI model boasting near-perfect accuracy at a massive urban academic center might fail completely in a rural clinic due to differing scanner calibrations and population demographics. We emphasize why the field must transition away from retrospective "vanity metrics" and toward clinically trustworthy prospective validation.• The Virtual Cell Paradigm: A staggering look into the near future where AI constructs completely accurate, computationally interactive digital twins of a patient's cancer. This framework allows doctors to test different drug regimens and simulate cellular responses mathematically in silico before ever administering medicine to the actual patient.Key Takeaway: Multimodal AI proves that cancer diagnostics must go beyond isolated data points. By dynamically synthesizing highly fragmented clinical information and utilizing synthetic imaging to overcome rare disease data scarcity, AI is pushing oncology into an era of robust, individualized molecular phenotyping. Ultimately, these innovations are replacing risky, invasive testing with precSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailPaper Discussed in this Episode:Spatial omics and AI for clinically actionable cancer biomarkers. Reitsam NG. PLoS Med 2026; 23(4): e1005049.Episode Summary: In this deep dive, we explore how artificial intelligence and spatial omics are fundamentally rewriting the rules of cancer diagnostics. We break down a 2026 editorial that challenges a deceptively simple question driving modern oncology: Is a tumor "positive" or "negative" for a biomarker? As targeted cancer therapies evolve, this binary thinking is failing us. We discuss why mapping where and how much of a therapeutic target exists is crucial, and how AI is stepping in to solve the reproducibility issues human pathologists face when making borderline diagnostic calls.In This Episode, We Cover:• The Illusion of "Positive" vs. "Negative": Why the basic premise of modern cancer therapies—like antibody-drug conjugates (ADCs)—often falls apart in reality when we ignore the spatial heterogeneity of a tumor.• The Power of Computational Pathology: How AI is transforming subjective, qualitative estimates into continuous, reproducible data, scaling the quantification of complex biomarkers like PD-L1 and TROP2.• "Virtual" Proteomics: The fascinating concept of using AI models to infer high-dimensional spatial information and immune maps directly from standard, routine H&E stained slides.• The HER2 Bottleneck: A real-world look at the breast cancer drug T-DXd, which now demands pathologists distinguish between "HER2-low" and "HER2-ultralow". While human agreement drops below 70% at these fuzzy decision boundaries, AI steps up with a staggering ~97% sensitivity.• Three Shifts for the Future: Why clinical trials and routines must adopt continuous measures (like percentage of expressing cells), demand longitudinal repeat testing at disease progression, and utilize adaptive trial platforms.• Bridging the Gap to Reality: The massive hurdles preventing widespread adoption—such as equipment costs exceeding $250,000 and massive data storage needs. We discuss why a hybrid workflow that bolsters routine pathology with deployable AI is the best path forward to prevent widening global health disparities.Key Takeaway: The future of precision oncology isn't just about finding new drug targets; it's about fundamentally changing how we measure them. By moving away from rigid binary thresholds and using AI to map the continuous, spatial reality of tumors, we can unlock the true potential of targeted therapies. However, achieving this diagnostic ecosystem requires overcoming significant financial and systemic hurdles—such as updating reimbursement pathways and proficiency testing—to ensure these life-saving insights are accessible across all healthcare settings.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

Spiritual Warfare I'm giving away FREE E-copies of my book. Email me your email address at spiritualwar17@gmail.com for your FREE E-copy. If you have questions or would like us to speak about a certain topic email bradshaw_jc@icloud.comWe will try to answer them as quickly as possible on the podcast.  If you would like to support this Podcast monthly or make a one-time gift you can click the PayPal link below. https://paypal.me/spiritualwar17

Send us Fan MailIn our last episode, we celebrated five years of conversations around healing, faith, and what it means to reclaim your story, and today we're beginning to step into more conversations that really matter.This episode is all about understanding trauma through a lens of compassion, because when we begin to see what's really going on beneath the surface, it changes the way we see ourselves, others, and even God.Before we dive in, I want to thank our sponsor for today's episode, Tim and Andrea Looney from The Looney Advantage at Realty Executives. They've been serving families across the Tucson area for over 24 years, helping people buy and sell homes with care and integrity. Tucson friends, you can find them by searching The Looney Advantage on Facebook.Here are two FREE Ebooks for you! 1. Shame Off You: 10 steps to shattering shame in your life, HERE. 2. ABC's: CLICK HERE for a FREE E-book to help you combat lies and replace them with God's truth.  For more encouragement, check out some of our offerings at www.reclaimedstory.comDid you know we have a jewelry line that speaks to your identity in Jesus? CLICK HERE to shop. Every purchase helps support our mission to provide healing and hope to women worldwide. Would you partner with us to spread the message of hope and healing? You can DONATE HERE.  Living the Reclaimed Life is a Reclaimed Story, Inc. podcast, An Arizona non-profit corporation. If you would like to connect with a safe group of women doing real-life together, join our private Facebook page, “Living the Reclaimed Life” or on  Facebook  or Instagram 

Send us Fan MailI did something I've never done before for this episode — I went live from the middle of a national park. This is DigiPath Digest #42, broadcasting from the Great Sand Dunes National Park in Colorado via Starlink from my family road trip. Yes, it actually worked. And so did the papers.This episode covers four papers that all ask the same uncomfortable question from different angles: how close is AI to being genuinely useful in real pathology practice — and what's still standing in the way? From LLMs interpreting cervical Pap smears, to AI guiding breast cancer treatment decisions from a simple H&E slide, to a practical roadmap for bringing generative AI into oncology workflows — this one covers a lot of ground.I also introduced something new: my AI-powered paper summary podcast subscription. For $7 a month, AI hosts summarize digital pathology literature in a journal-club style so you can stay current without spending hours reading abstracts. I walk through how it works and why I built it.What we cover:[00:00] Going live from the wilderness — Starlink, sand dunes, and a very cold morning[02:01] How I use AI-generated audio summaries to prep for each DigiPath Digest[03:19] Paper 1: Can LLMs like ChatGPT and Gemini interpret cervical cytology? Spoiler: ~47–48% exact concordance — promising, but not there yet[10:23] Bonus: My new AI-powered paper summary subscription — $7/month, journal-club style[14:05] Paper 2: AI in oral oncology — CNNs for early lesion detection, multimodal prognostics, and the real barriers still blocking clinical adoption[20:28] Paper 3: Generative AI in oncology — from chat tools to agentic EHR-integrated assistants, and why augmentation is the goal, not automation[25:35] Paper 4: Computational pathology in breast cancer — predicting BRCA1/2, HER2, Oncotype DX, and treatment response from standard H&E slides[31:39] Final thought: the floor just got raised for all of us — how I think about new technology in pathologyResources & Links:Paper 1 – LLMs & Cervical Cytology (PubMed): https://pubmed.ncbi.nlm.nih.gov/41931983/Paper 2 – AI in Oral Oncology (PubMed): https://pubmed.ncbi.nlm.nih.gov/41930554/Paper 3 – Generative AI in Oncology Practice (PubMed): https://pubmed.ncbi.nlm.nih.gov/41930309/Paper 4 – AI & Digital Pathology in Breast Cancer (PubMed): https://pubmed.ncbi.nlm.nih.gov/41930306/Watch on YouTube: https://www.youtube.com/live/O2hOU4gM0Bk?si=oH8iJ8HiBb29USG3Digital Pathology Place: https://www.digitalpathologyplace.comSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailPaper Discussed in this Episode:How to bring generative AI to oncology practice. D. Truhn & J. N. Kather. ESMO Real World Data and Digital Oncology 2026.Episode Summary:In this journal club deep dive, we step out of the theoretical sci-fi hype of artificial intelligence and look at a practical, real-world roadmap for bringing Generative AI into oncology. We examine a 2026 paper that maps out the trajectory for deploying Large Language Models (LLMs) to combat the overwhelming cognitive load of modern cancer care. Rather than replacing clinicians, this episode explores how AI can synthesize massive amounts of unstructured data—like dense pathology narratives and shifting molecular reports—so doctors can get back to practicing medicine instead of acting as data entry clerks.In This Episode, We Cover:• The Data Avalanche in Oncology: Why the shifting landscape of decades of patient histories, clinical trial registries, and handwritten notes creates an information load that human cognition simply wasn't evolved to process all at once.• How LLMs Actually "Think": Why predicting the "next word" based on massive training data allows AI to mimic medical reasoning and organize complex clinical concepts—like linking a BRAF mutation directly to a specific inhibitor without looking up a rulebook.• The Three Evolutionary Steps of AI Complexity: ◦ Step 1: Stand-alone Models: The "closed-book exam." These models (like early ChatGPT) are frozen in time with their original training data and have zero access to new clinical trials or FDA updates. ◦ Step 2: Retrieval-Augmented Generation (RAG): The "open-book exam." The AI searches continually updated external databases and guidelines before answering, significantly reducing fabricated answers, or "hallucinations". ◦ Step 3: Agentic AI: The ultimate goal. Fully functioning "research assistants" that can iteratively reason, plan steps, and invoke external software tools (like lab APIs and medical calculators) to complete complex tasks like proposing tumor board summaries.• The Deployment Roadblocks: Why you can't just drop an autonomous agent into a fragmented hospital IT network built in 2005. We unpack strict security silos, audit logs, and the dangerous reality of "domain shift"—where an AI trained perfectly at Johns Hopkins might silently fail at a community clinic simply due to different doctor shorthand or microscopic slide scanner colors.• The Human Element & Automation Bias: The hidden dangers of junior doctors losing their clinical intuition (deskilling) and why system design must force the AI to "show its work" with intentional friction to prevent doctors from blindly clicking accept on a hallucinated treatment plan.• Your Edits Are the Future: A fascinating look at how a clinician's daily administrative annoyances—every strike-through and manual correction of an AI draft—serve as the ultimate, high-value ground-truth data to train the next generation of oncology AI.Key Takeaway:The destination we are driving toward is augmentation, not automation. By handling massive information synthesis, uncovering patterns, and explicitly showing its work, AI can act as a tireless assistant that improves routine care, while leaving the final, nuanced clinical judgment exactly where it belongs: with the human physician.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailPaper Discussed in this Episode: Can large language models like ChatGPT and Gemini interpret cervical cytology accurately? Saroja Devi Geetha. Annals of Diagnostic Pathology 2026; Volume 83, 152641.Episode Summary: In this journal club deep dive, we explore what happens when advanced artificial intelligence is thrown into the visually chaotic realm of human biology. We examine a 2026 study evaluating whether two massive multimodal models—GPT-5 and Gemini 2.5 Pro—can accurately read digital cervical Pap smears without any prior fine-tuning,,. We unpack how these general-purpose models perform on highly specialized visual tasks, revealing that while they aren't ready to fly solo, they exhibit fascinating and distinct diagnostic "personalities" that will undoubtedly reshape the future of the pathology lab,.In This Episode, We Cover:• The "Textbook" Test Setup: How researchers tested the baseline visual reasoning of GPT-5 and Gemini 2.5 Pro by feeding them 100 curated, gold-standard digital Pap test images from the Hologic Education Site to classify using the Bethesda System,,.• The Clinical Reality Check: While the models only achieved a coin-toss exact diagnostic match rate (47% for GPT-5 and 48% for Gemini), their accuracy jumped to 66% when evaluating clinical management protocols—proving they are beginning to grasp the underlying severity and medical consequences of cellular abnormalities,,.• The Over-Anxious Resident (Gemini 2.5 Pro): Gemini acted like a highly sensitive but unrefined trainee, hitting 84% sensitivity and expertly spotting infectious organisms (71%),,. However, its tendency to confuse dense, overlapping cellular clumps with high-grade squamous intraepithelial lesions (HSIL) led to massive overcalling, dragging its specificity down to 71% and creating a risk of false alarms,.• The Big-Picture Academic (GPT-5): GPT-5 proved to be much more measured, demonstrating better overall specificity (74%) and excelling at identifying subtle structural shifts like low-grade squamous intraepithelial lesions (LSIL) (75%) and glandular changes,. Yet, in its focus on the big picture, it completely missed obvious infectious organisms, scoring a dismal 20%,.• The Future of the Lab - Prompt Engineering & The Algorithmic Auditor: Why the next era of cytopathology requires rigorous AI fine-tuning on proprietary datasets and cytology-specific prompt optimization. We discuss a major paradigm shift where human pathologists may transition from actively hunting for disease to acting as "algorithmic auditors" whose primary job is to filter out the hyper-vigilant machine's noise,.Key Takeaway: Current multimodal LLMs are not yet reliable for independent Pap test interpretation due to critical blind spots and tendencies to overcall lesions,. However, their out-of-the-box performance establishes a staggering baseline. By understanding their unique mechanical flaws, pathologists can prepare to use these systems as highly effective co-pilots, seamlessly combining the algorithm's computational brute force with the indispensable filter of human medical reasoningSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailPaper Discussed in this Episode: Artificial intelligence in oral oncology: Current advances and future potential in diagnosis, prognosis, and therapeutic decision-making. Annamalai A, Dhanes V, Jayalakshmi L, Shanmugam R, Ravi S. Cancer Treatment and Research Communications 47 (2026) 101193.Episode Summary: In this journal club deep dive, we explore how AI is fundamentally reshaping the clinical management of Oral Squamous Cell Carcinoma (OSCC). We examine a comprehensive March 2026 study that confronts a frustrating paradox: despite the oral cavity being visible to the naked eye, OSCC survival rates have stagnated due to late-stage diagnosis and complex tumor biology. This episode breaks down how algorithms are moving oncology from a reactive discipline to a highly predictive, personalized science.In This Episode, We Cover:• The OSCC Paradox: Why relying on traditional visual inspection and standard TNM staging ignores biological heterogeneity, and how AI steps in where the naked eye and basic anatomy fall short.• Pocket Pathologists: The revolutionary use of Convolutional Neural Networks (CNNs) in smartphone apps and portable devices, achieving up to 82% to 92% sensitivity for point-of-care screening in resource-constrained settings.• The Committee of Algorithms: How AI acts as a "multimodal synthesizer," fusing radiomics (tumor texture), histopathology (tumor-infiltrating lymphocytes), genomics, and Natural Language Processing (NLP) of unstructured clinical notes to predict individualized risk.• Real-Time Margin Guidance: How AI combined with fluorescent imaging provides surgical margin feedback to surgeons in the operating room in under five minutes with over 85% concordance with expert histopathologists.• Digital Twins: The sci-fi reality of running virtual clinical trials. We discuss how AI uses reinforcement learning to build simulated patient copies, allowing tumor boards to predict radiotherapy outcomes and drug toxicities before treating the physical person.• The Black Box, Bias, and the Fix: The major roadblocks preventing immediate clinical rollout. We discuss opaque decision-making and training data bias (which can drop accuracy by over 15% in underrepresented groups). We also explore the solutions: Explainable AI (Grad-CAM heat maps) to visualize decision logic, and Federated Learning (privacy-preserving decentralized training) to eliminate data sharing hurdles.Key Takeaway: The true value of AI in oral oncology isn't in replacing human clinicians, but in digesting massive multi-omics data that no single human could synthesize alone. By acting as a transparent, explainable support tool, AI is setting the stage for a future where tomorrow's healthcare professional might spend as much time treating a virtual patient as the physical one sitting in the chairSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailPaper Discussed in this Episode: How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer. T. Ruelle, T. Grinda, L. Del Mastro, M. Lacroix-Triki, B. Pistilli & G. Gessain. ESMO Real World Data and Digital Oncology 2026.Episode Summary: In this journal club episode, we step into the reality of computational pathology and explore how artificial intelligence is fundamentally transforming breast cancer diagnostics. We examine a comprehensive review detailing how AI not only assists overburdened healthcare systems but also unlocks invisible genomic data straight from a standard $5 hematoxylin-eosin (H&E) glass slide. What happens when a machine can predict complex DNA mutations just by evaluating the structural architecture of cells?In This Episode, We Cover:• The Diagnostic Bottleneck: Understanding the critical worldwide shortage of pathologists colliding with a projected 3.2 million global breast cancer diagnoses by 2050, and why the system is under unprecedented strain.• The Biomarker Battle: Why the human visual cortex struggles to quantify faint immunohistochemistry stains, and how AI acts as a perfect "digital colorimeter". We discuss its near-perfect concordance in assessing crucial biomarkers like Ki-67, ER, PR, PD-L1, and the newly established HER2-low status.• Seeing the Invisible (Predictive AI): How deep learning transcends visual diagnostics to predict treatment outcomes, such as a patient's response to neoadjuvant chemotherapy. We also discuss AI's ability to infer Homologous Recombination Deficiency (HRD) and BRCA1/2 mutations by identifying macroscopic footprints like laminated fibrosis.• Decoding Genomic Assays: The potential to replace expensive, tissue-consuming genomic tests like Oncotype DX with AI models (such as Orpheus) that predict recurrence risk straight from digitized slides, achieving accuracy that rivals the tests themselves.• Roadblocks to Reality: The major clinical friction preventing global rollout. We discuss the steep infrastructure costs of whole-slide scanners, the danger of AI bias across diverse hospital datasets, and the ethical "black box" problem requiring the evolution of transparent, agent-based AI.Key Takeaway: Computational pathology is moving far beyond basic diagnostic assistance. By successfully reading the structural language of biology, AI proves it can extract costly, invisible molecular data from standard biopsies, fundamentally changing the economics and accessibility of global personalized healthcareSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

Spiritual WarfareI'm giving away FREE E-copies of my book. Email me your email address at spiritualwar17@gmail.com for your FREE E-copy. If you have questions or would like us to speak about a certain topic email bradshaw_jc@icloud.comWe will try to answer them as quickly as possible on the podcast.  If you would like to support this Podcast monthly or make a one-time gift you can click the PayPal link below. https://paypal.me/spiritualwar17

Send us Fan MailYou don't need a fancy scanner, a huge budget, or a computational background to get started in digital pathology. That's what I told the ACVP podcast — and I meant it. In this episode, I share my full digital pathology journey: from being completely intimidated by scanners during residency, to building a career that combines toxicologic pathology, image analysis, and remote work at a global CRO.If you're a resident, a trainee, or even a seasoned pathologist who hasn't fully stepped into the digital space yet — this one's for you.We talked about practical ways to get started, what foundation models actually mean for our daily work, how to build a team when implementing digital pathology at your institution, and why change management might be the most underestimated skill in this whole process.What we cover:[00:00] My background — from veterinary school in Poland to digital pathology[03:22] Why I chose industry over academia, and what that transition looked like[05:02] How a simple IHC side project became my entry point into digital pathology[07:11] How digital slides helped me pass my boards — and fall back in love with histopathology[10:24] My first job at a digital pathology image analysis company[12:00] What my current role at Charles River Laboratories looks like day-to-day[13:53] The best free resources for trainees to start exploring digital slides RIGHT NOW[15:26] Why pathologists need to understand image analysis principles — segmentation, classification, object detection[19:31] Foundation models, transformer architecture, and why annotation bottlenecks may soon be a thing of the past[24:13] Practical advice for institutions implementing digital pathology — equipment, teams, and managing resistance to change[27:30] How I unplug: trail running, weight training, and pathology-themed earringsResources & Links:Joint Pathology Center (JPC) digital slides: https://www.jpc.orgDavis Thompson Foundation — Noah Slidebox: https://www.davisthomasonfoundation.orgQuPath (free, open-source image analysis): https://qupath.github.ioDigital Pathology Place: https://www.digitalpathologyplace.comWatch the full conversation on YouTube: https://youtu.be/wTDdlxJzq-A?si=xkz5YNljrUX5SnhdSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailHow close is pathology AI to making decisions that matter in real workflows, real trials, and real patient care?In this episode of DigiPath Digest, I review five recent papers that approach that question from very different angles. We look at multimodal survival prediction in cervical cancer, pathology-driven response assessment in neoadjuvant immunotherapy for head and neck squamous cell carcinoma, AI-assisted Ki-67 scoring in pulmonary neuroendocrine neoplasms, automation and AI in hematologic diagnostics, and AI-based qFibrosis readouts from the Phase 3 MAESTRO-NASH trial.What I liked about this set of papers is that they do not all tell the same story. Some show clear progress. Some show where AI already works well as an adjunct. Others make it very clear that validation, governance, reproducibility, and workflow design still matter just as much as model performance.Key topics and timestamps00:00 Introduction, Easter edition, and community updates 00:51 USCAP recap, signed book giveaway, and free Digital Pathology 101 PDF 02:04 Partnerships, lab automation preview, and what's coming in this episode 03:25 Multimodal deep learning for cervical cancer survival prediction 13:00 Why pathology may be a better response endpoint than radiology in neoadjuvant HNSCC immunotherapy 23:09 Ki-67 scoring in pulmonary neuroendocrine neoplasms: pathologists vs two AI systems 33:46 AI, digital morphology, and automation in hematologic diagnostics 43:29 qFibrosis, digital biomarkers, and the MAESTRO-NASH Phase 3 trial 51:57 Closing thoughts, community updates, and Easter promotion Resources Deep Learning Can Predict the Overall Survival of Cervical Cancer Based on Histopathological Image, Gene Mutation and Clinical Information https://pubmed.ncbi.nlm.nih.gov/41902378/ Modern Pathology-Driven Strategies in Neoadjuvant Immunotherapy for Head and Neck Squamous Cell Carcinoma: From Residual Tumor Quantification to Spatial and AI-Based Biomarkers https://pubmed.ncbi.nlm.nih.gov/41899621/ Ki-67 Proliferation Index in Pulmonary Neuroendocrine Neoplasms: Interobserver Agreement Among Pathologists and Comparison of Two Artificial Intelligence-Based Image Analysis Systems https://pubmed.ncbi.nlm.nih.gov/41898274/ Molecular Pathology, Artificial Intelligence, and New Technologies in Hematologic Diagnostics: Translational Opportunities and Practical Considerations https://pubmed.ncbi.nlm.nih.gov/41897649/ Quantitative regression of qFibrosis with resmetirom: Exploratory histologic endpoints from the MAESTRO-NASH phase III clinical trial https://pubmed.ncbi.nlm.nih.gov/41895606/Support the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailPaper Discussed in this Episode:Reliable classification of polyps based on artificial intelligence: a development and validation study. Julbø FMI, Henriksen AL, et al. eClinicalMedicine 2026;93: 103826.Episode Summary:In this journal club deep dive, we explore a groundbreaking 2026 study that tackles the massive bottleneck in gastrointestinal pathology caused by successful colorectal screening programs. We examine POLARIS, an AI triage system designed to safely clear over 50% of a pathologist's routine workload. But what happens when the algorithm fiercely disagrees with the human diagnosis? In a blinded showdown, the AI proves it's not just an efficiency tool—it might just be the ultimate safety net for catching high-risk cancer cells that human eyes overlook.In This Episode, We Cover:• The Pathology Bottleneck: Why the success of colorectal screening programs is drowning labs in biopsy slides, and how the subjective, visual nature of diagnosing polyps leads to dangerous inter-observer variability.• The 5:2 Triage Strategy: How POLARIS categorizes gigapixel slide images into five biological classes (0 to 4) and translates them into two highly actionable buckets: "Review" (the complex and malignant) and "No Review Required" (normal tissue and routine tubular adenomas with low-grade dysplasia).• Beating the "Clever Hans" Effect: How researchers prevented the AI from "cheating" by recognizing the digital fingerprints of different scanner brands, like Aperio vs. NanoZoomer. By using an image registration tool called elastix to perfectly align slides scanned on both machines, they heavily penalized the algorithm mathematically for relying on color profiles, forcing it to focus purely on biological morphology.• The Showdown - Humans vs. AI: A blinded consensus review was conducted on 40 highly contentious cases where the AI aggressively disagreed with the original patient medical record. Three independent expert pathologists were brought in to break the tie without knowing the AI's or the original doctor's diagnosis.• The Shocking Results: The expert panel sided with the AI over the original human diagnosis in a staggering 92.5% of the disputed cases, proving the established clinical "ground truth" isn't infallible.• The RGBA Heat Map: How POLARIS functions as an active assistant, leaving normal tissue transparent (scaling the alpha channel to zero) while highlighting severe cellular atypia in glowing red, acting as a hyper-accurate topographical map for pathologists.Key Takeaway:AI in digital pathology isn't about autonomously replacing human experts; it's a hyper-sensitive navigational aid. By safely managing the flood of routine low-grade cases and accurately highlighting hidden high-risk dysplasias that exhausted human eyes miss, POLARIS corrects human errors and elevates the baseline standard of diagnostic care across the entire pipeline.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailPaper Discussed in this Episode: High-Sensitivity Pan-Cancer AI Assessment of Lymph Node Metastasis via Uncertainty Quantification. Wang X, Chen Y, Liu X, et al. npj Digit. Med. (2026).Episode Summary: In this episode, we explore a groundbreaking 2026 study that tackles the "black box" problem of medical AI. We dive into UPATHLN, a pan-cancer AI platform for detecting lymph node metastases that doesn't just try to be right—it explicitly knows when it might be wrong. By using an innovative "uncertainty" fail-safe, this system achieved an unprecedented 100% sensitivity while drastically cutting down pathologist workload.In This Episode, We Cover:• The Needle in the Haystack Problem: Why finding cancer in lymph nodes is crucial for patient survival and therapeutic decision-making, and why the sheer volume of rising cancer cases is overwhelming human pathologists.• The Danger of "Overconfident Errors": How standard deep learning models stumble on rare, "long-tail" tumor variants. Standard AI is prone to making incorrect predictions with high certainty on data it hasn't seen before, leading to dangerous missed diagnoses.• Meet UPATHLN - The Unified AI: Moving away from fragmented, organ-specific AI to a single, foundation-model-powered platform trained and validated on a massive dataset of 26,229 lymph nodes across 14 distinct primary organs.• The "Fail-Safe" Mechanism (Uncertainty Estimation): How the researchers built a decoupled module that acts as a clinical safety net. Instead of forcing a guess, the AI flags "High Uncertainty" (HU) regions—like atypical cells or distracting elements like anthracotic pigment—and routes them directly for mandatory human review.• The Results - 100% Rescue Rate: In independent testing, relying on the AI's diagnostic probability alone would have missed 60 metastases. However, the uncertainty module successfully intercepted all 60 of these initially missed cases, achieving a 100% conditional sensitivity, even on 7 rare cancer types the AI had never seen before during training.• The Future of the Lab: How UPATHLN safely eliminated 73.2% of negative lymph nodes from manual review. By liberating pathologists from routine triage, the system frees up time for advanced, multi-dimensional precision oncology that goes beyond simple staging.Key Takeaway: The key to safe clinical AI isn't just raw accuracy—it's failure awareness. By teaching AI to explicitly model its own uncertainty, the system intercepted all missed diagnoses, handled rare biological variants safely, and established a trustworthy, workload-efficient partnership between human experts and artificial intelligenceSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailPaper Discussed in this Episode: A Deep Learning Framework for Automated Triage of Breast Cancer Biopsies in Malaysia: A Simulation Study to Reduce Resource Consumption and Diagnostic Turnaround Time. Yudi Kurniawan Budi Susilo, Dewi Yuliana, Shamima Abdul Rahman, Siew Lian Leong. Clinical Breast Cancer 2026.Episode Summary: In this deep dive, we explore a revolutionary approach to a massive real-world healthcare bottleneck: agonizingly long diagnostic wait times in resource-constrained public hospitals. We unpack a 2026 study that bypasses strict patient privacy red tape by using AI trained entirely on synthetic, computer-generated breast tissue images. More importantly, the researchers built a "digital twin" of a Malaysian hospital to prove how an AI triage system could reorganize the diagnostic queue, catching aggressive cancers much faster while effectively conjuring new specialists out of thin air through massive time savings.In This Episode, We Cover:• The "FIFO" Bottleneck: Why the traditional First-In, First-Out workflow traps critical malignant biopsies behind a mountain of benign cases (which make up 70-80% of biopsies), acting like a trauma surgeon forced to treat paper cuts before looking at a major emergency.• Solving the Data Paradox with GANs: How the team used Generative Adversarial Networks (StyleGAN2-ADA) to forge 10,000 synthetic whole slide images, achieving such high statistical realism (FID < 25) that human pathologists were fooled and gave a >90% plausibility rating.• The AI Triage Engine: A look into the Convolutional Neural Network built on a pre-trained ResNet50 architecture. We discuss how it uses an attention-based Multiple Instance Learning (MIL) mechanism to break down billions of pixels into digestible patches, achieving a staggering 96.5% sensitivity—acting as a hyper-vigilant gatekeeper to ensure no cancers are missed.• Sim City for Pathology: How the researchers avoided testing on a live clinic and instead ran a Discrete-Event Simulation mimicking a chaotic public hospital for 250 days, factoring in chaotic arrival times and human reading delays.• The Shocking Results: The pure AI triage system plummeted turnaround time for suspicious cases by 38.3% (dropping from 7.24 days to 4.47 days), vastly outperforming hybrid or rule-based systems.• The Ripple Effect (Green Labs & Burnout): The system slashed pathologist workloads by 22.5% (saving 422 specialist hours annually) and reduced chemical reagent consumption by 15.2% by batch-processing the benign queue with standard chemicals.• The Reality Check: The critical limitations of synthetic data when faced with the messy realities of a physical hospital, including varying digital scanner color calibrations, IT infrastructure crashes, and local histological edge cases.Key Takeaway: AI in medicine isn't just about making the diagnosis—it's about fixing the workflow. By combining hyper-realistic synthetic data generation with discrete-event simulation, researchers proved that simply allowing an algorithm to sort a hospital's backlog can cut agonizing wait times for cancer patients by 38.3% and significantly relieve overburdened medical staff. The digital twin of the hospital is already here, and it might just hold the cure for systemic healthcare gridlockSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailPaper Discussed in this Episode: Molecular Pathology, Artificial Intelligence, and New Technologies in Hematologic Diagnostics: Translational Opportunities and Practical Considerations. Alnoor F, Mukherjee S, Menon MP, Ng D, Li P, Ohgami RS. Diagnostics 2026.Episode Summary: In this deep dive, we explore how hematology labs are tackling a massive rise in diagnostic complexity combined with persistent staffing shortages. The solution isn't just working harder—it's an entirely new workflow powered by robotics and AI. We unpack a comprehensive 2026 review that looks at the cutting-edge transformation of hematopathology, moving from manual microscopes to collaborative robots (cobots), digital morphology, and AI-driven genomic analysis. Can machines handle the grueling pre-analytical work and help experts diagnose leukemia faster and more accurately?In This Episode, We Cover:• The Modern Lab Crisis: How the latest WHO and International Consensus Classification (ICC) frameworks demand high-volume, multi-modal genomic and morphologic data, stretching human pathologists to their limits.• Enter the "Cobots": Collaborative robots are taking over the repetitive benchwork. We discuss systems like the UR5 cobots in Denmark that sort 3,000 blood tubes a day, and the Pramana Spectral HT robotic-arm scanners that digitize over 1,000 slides daily, freeing up human staff for higher-level tasks.• The Digital Eye (Morphology & AI): How platforms like CellaVision and Scopio turn glass slides into AI-analyzed data. ◦ Peripheral Blood: AI pre-classifies cells with 85-98% concordance to manual microscopy, prioritizing blasts and abnormal cells for expert review to improve efficiency. ◦ Bone Marrow: Deep learning isn't just counting cells; it's accurately quantifying reticulin fibrosis and identifying leukemia subtypes with human-level performance.• Flow Cytometry Gets an Upgrade: High-dimensional flow cytometry data meets deep learning. AI models are now achieving expert-level performance in classifying mature B-cell neoplasms and accurately distinguishing acute leukemias from non-leukemic samples.• The Molecular Frontier: AI is making sense of complex genomic datasets. We discuss breakthroughs like the MARLIN neural network, which achieves rapid epigenomic classification of acute leukemia in under two hours, and how AI assists in tracking measurable residual disease (MRD) longitudinally.• The Economics of Automation: Digital pathology is a smart financial investment. We review projections showing potential savings of $18 million over five years for integrated health systems, driven by improved efficiency, higher throughput, and fewer diagnostic errors.Key Takeaway: The integration of artificial intelligence and robotics is not meant to replace hematopathologists; rather, these technologies serve as essential scaling tools designed to absorb grueling physical labor and routine analytical tasks. By building a workflow where machines handle the sorting, scanning, and initial pattern recognition, experts can focus their time on final diagnostic synthesis—ultimately delivering faster, more precise patient careSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailPaper Discussed in this Episode:Quantitative regression of qFibrosis with resmetirom: Exploratory histologic endpoints from the MAESTRO-NASH phase III clinical trial. Schattenberg JM, Bedossa P, Guy CD, et al. Journal of Hepatology 2026; https://doi.org/10.1016/j.jhep.2026.03.021.Episode Summary: In this deep dive, we explore how artificial intelligence is revolutionizing the way we measure liver disease recovery. We examine a groundbreaking 2026 Phase III clinical trial (MAESTRO-NASH) that compared traditional human pathologist staging against an AI-driven digital pathology tool called qFibrosis. The study forces us to reconsider our clinical gold standards by asking: what if AI can detect subtle biological healing that the experienced human eye completely misses?In This Episode, We Cover:• The Silent Epidemic: Understanding Metabolic dysfunction-associated steatohepatitis (MASH), a progressive, active form of fatty liver disease linked to cardiovascular risk and cirrhosis. We discuss why precisely tracking the reversal of liver fibrosis is crucial for patient outcomes.• The "Ordinal" Problem: Why the current "gold standard"—human pathologists assigning a simple ordinal score (like Stage F1, F2, or F3)—is subjective and fails to capture the dynamic, nuanced reality of fibrosis progression and regression.• The AI Microscope (SHG & qFibrosis): ◦ SHG (Second Harmonic Generation): An imaging technique that takes advantage of the physical properties of collagen to map out the three-dimensional architecture of the liver. ◦ qFibrosis: An AI-driven analysis tool that evaluates up to 184 distinct features of liver collagen (like string length, width, and intersections) across different regions of the liver lobule, providing a continuous, hyper-detailed assessment rather than a basic category.• The Showdown - Humans vs. AI: Using data from 966 patients in the MAESTRO-NASH trial, we compare how human pathologists and the AI evaluated liver biopsies at baseline and week 52 to test the efficacy of the drug resmetirom.• The AI's "Aha!" Moment (Seeing the Invisible): The most shocking finding of the study occurred in the "non-responder" group. Even when human consensus reads declared certain patients had no histological improvement, the AI detected significant, continuous reductions in liver fibrosis (qFC scores). The digital pathology tool was able to pick up on subthreshold, early matrix remodeling that was entirely invisible to standard manual scoring.• Mapping the Liver's Healing: The AI proved its biological accuracy by successfully linking its spatial data to real-world clinical outcomes. The AI found that specific regional changes—particularly in the portal tract—strongly correlated with non-invasive liver stiffness tests like Magnetic Resonance Elastography (MRE).Key Takeaway: AI isn't here to replace human pathologists; it is a hyper-sensitive tool designed to uncover hidden data patterns. By detecting continuous, region-specific changes in liver collagen, AI digital pathology can identify early therapeutic responses to MASH treatments that traditional staging misses, fundamentally changing how we track disease reversal and personalize medicineSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailPaper Discussed in this Episode:Ki-67 Proliferation Index in Pulmonary Neuroendocrine Neoplasms: Interobserver Agreement Among Pathologists and Comparison of Two Artificial Intelligence-Based Image Analysis Systems. Teoman G, Turkmen Usta Z, Sagnak Yilmaz Z, Ersoz S. MDPI 2026.Episode Summary:In this journal club deep dive, we step into the lab to examine a direct comparison between expert human pathologists and artificial intelligence. We explore a 2026 study that evaluates how two different AI image analysis systems score the critical Ki-67 biomarker in Pulmonary Neuroendocrine Neoplasms (PNENs) alongside four experienced human experts. Unlike stories where AI and humans clash, this study explores a different exciting reality: Can AI perfectly match the human gold standard to automate and standardize a highly tedious, labor-intensive medical process?In This Episode, We Cover:• The Diagnostic Challenge of Lung NENs: Understanding Pulmonary Neuroendocrine Neoplasms, a biologically diverse group of lung tumors ranging from slow-growing typical carcinoids to highly aggressive large cell neuroendocrine carcinomas. We discuss why precise classification is critical for predicting patient outcomes and guiding treatment.• The Spotlight Biomarker (The Speedometer): ◦ Ki-67: The definitive marker of active cellular proliferation, essentially acting as the tumor's "speedometer". While not formally incorporated into the WHO grading criteria for lung NENs, it is a vital clinical tool used to distinguish low-grade from high-grade tumors and identify biologically aggressive lesions.• The Showdown - Humans vs. AI: Four experienced pathologists go head-to-head with two digital heavyweights—the Roche uPath Ki-67 and the Virasoft Virasight Ki-67 algorithms. They analyzed 63 cases across different tumor subtypes, meticulously evaluating approximately 2,000 cells per predefined tumor hotspot.• Round 1 - Impressive Human Concordance: The human experts achieved near-perfect interobserver agreement (an Intraclass Correlation Coefficient of 0.998) when utilizing pre-selected hotspot regions, proving that standardized manual counting by experts is highly reliable.• Round 2 - AI Meets the Gold Standard: Both AI systems demonstrated massive, statistically significant correlations with the human experts' assessments. The AI reliably stratified the lung tumors into low, intermediate, and high-risk clinical categories without systematic bias, proving the algorithms can match human accuracy.• The Future of the Lab: Why AI shouldn't replace pathologists, but rather serve as a reproducible, objective assistant in the pathology lab. We discuss how automated AI analysis can reduce observer fatigue, enable rapid assessment of large tumor areas, and standardize testing across institutions, despite current roadblocks like algorithm complexity and a lack of wide accessibility.Key Takeaway:Artificial intelligence doesn't have to disagree with humans to prove its profound clinical worth. By successfully matching the excellent accuracy of top pathologists, these AI systems proved they can reliably handle the exhausting, subjective task of tumor cell counting. This paves the way for faster, highly standardized tumor evaluation, which could ultimately lead to more consistent and reliable prognostic diagnoses for lung cancer patientsSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailDeep Learning Can Predict the Overall Survival of Cervical Cancer Based on Histopathological Image, Gene Mutation and Clinical Information. Shen J, Miao Z, Wang L, et al. IET Systems Biology 2026.Episode Summary: In this deep dive, we explore a groundbreaking 2026 study that uses multimodal deep learning to act as a "master diagnostician" for cervical cancer. We examine what happens when an AI is fed a combination of standard clinical data, cutting-edge genetic sequencing, and century-old H&E tissue slides. The results force us to rethink how cancer operates: what happens when the genetic "blueprint" of a tumor lies to us, and the real biological truth is hiding in the seemingly chaotic pink and purple pixels of the connective tissue?In This Episode, We Cover:The Murky Diagnostics of Oncology: Understanding why predicting an individual patient's overall survival (OS) in cervical cancer is profoundly difficult. Getting this prediction wrong means risking either lethal undertreatment (distant metastasis) or subjecting stable patients to devastating overtreatment toxicities.The Three Modalities (The Suspect, The DNA, and The Security Footage):Clinical Data: The "suspect's description," utilizing standard patient metrics like age and tumor stage.Molecular Data: The genetic "blueprint" and somatic gene mutations. The AI isolated major red flags like RGR, DBN1, and CALCR mutations, which drive metastasis and signal poor prognosis.Histopathological Images (H&E): The "security footage" showing the physical tissue battlefield via whole slide images.The Model Showdown: Researchers trained a deep learning model (ResNet18) and fused these modalities using Multimodal Compact Bilinear (MCB) fusion. The AI was tasked with classifying patients into short-term (under 3 years) or long-term (over 3 years) survival, and it was rigorously validated on a completely independent dataset (PUMCH) to ensure generalizability.Round 1 - The Genetic Curveball: Despite being the cell's source code, genetic mutation data was the absolute worst predictor of survival, achieving an AUC of just 0.559. Adding it to the AI actually caused the "curse of dimensionality," making the model worse by overwhelming it with mathematical noise.Round 2 - The AI's "Aha!" Moment: The tissue phenotype dictates what actually happens. Fusing simple clinical data (age) with H&E images achieved a highly accurate 0.783 AUC. Even more shockingly, for aggressive short-term survival cases, the AI didn't focus heavily on the tumor itself. It looked at the stroma (connective tissue), deducing on its own that the host's inflammatory battleground dictates the lethality of the disease.The Future of the Lab: How automated quality control (HistoQC) and mathematical techniques (Macenko color normalization) strip away lab technician error and chemical dye variations. We also look ahead to how hyperspectral imaging might soon reveal the foundational chemical signatures of living cells.Key Takeaway: Throwing more data at an algorithm isn't always better. By successfully extracting profound biological truths from routine, inexpensive H&E slides, the AI proved that we don't necessarily need $1,000 genomic sequencing panels to accurately predict prognosis. The physical manifestation of the tumor microenvironment tells us exactly who is winning the battle, paving the way for accessible precision medicineSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailPaper Discussed in this Episode:Modern Pathology-Driven Strategies in Neoadjuvant Immunotherapy for Head and Neck Squamous Cell Carcinoma: From Residual Tumor Quantification to Spatial and AI-Based Biomarkers. Annabella Di Mauro, Rossella De Cecio, Saverio Simonelli, et al. Cancers (MDPI) 2026.Episode Summary: In this journal club deep dive, we explore a paradigm-shifting 2026 paper that fundamentally fractures our reliance on traditional radiology in head and neck cancer. We uncover a shocking clinical disconnect where seemingly devastating CT scans mask miraculous microscopic victories. When neoadjuvant immunotherapy unleashes the immune system, why does the tumor often look like it's growing on imaging? And how is pathology stepping out of the shadows to become the ultimate arbiter of biological truth, dictating precise surgical and medical oncology decisions?In This Episode, We Cover:The Trojan Horse of Imaging (Pseudoprogression): Why traditional CT scans are failing us in the immunotherapy era. Immunotherapy causes an influx of T-cells and inflammation that physically expands the tissue, tricking radiologists into diagnosing progressive disease when the cancer is actually being systematically dismantled from the inside out.The New Gold Standard - RVT: Why measuring the "shadow" of the tumor is obsolete. We discuss why pathologists are pivoting away from size and instead strictly quantifying Residual Viable Tumor (RVT) to determine the exact percentage of living, metabolically active carcinoma cells left behind.The "Starry Sky" Phenomenon: Tumors don't shrink like an ice cube melting from the outside in. We discuss how immune cells tunnel into the tumor, shattering it into a discontinuous "starry sky" pattern—scattered, radiologically occult microscopic islands of surviving cancer hidden across a vast sea of therapy-altered stroma.Compartmental Dissociation (The Nodal Force Field): A terrifying clinical reality where a patient can achieve a 100% complete pathological response at the primary mucosal site, but simultaneously harbor highly viable, proliferating cancer in their cervical lymph nodes. We explore how tumors hijack M2 macrophages to build a localized, cytokine-driven "force field" that neutralizes systemic T-cells the second they enter the node.The Future - High-Definition Spatial Biology: How AI-assisted digital pathology and spatial transcriptomics act as the "GPS tracking" or "sports analytics" of the tumor microenvironment. By mapping the exact coordinates of immune and cancer cells, tumor boards can confidently de-escalate toxic post-operative treatments for clear patients, or accurately target specific immunosuppressive resistance niches.Key Takeaway: Traditional imaging measures the volume of the battlefield, not the volume of the remaining enemy. By redefining therapeutic response through the microscopic lens of Residual Viable Tumor and AI-driven spatial biology, pathologists are no longer just staging dead tissue. They are now the central navigators of precision oncology, guiding the real-time escalation and de-escalation of patient care based on the true biological reality of the tumorSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

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Send us Fan MailWhat if the way you respond to life makes more sense than you think?In this special episode, Denisha is joined by Deborah and Robin as they celebrate five years of the Living the Reclaimed Life podcast and step into a new season of conversations together.Through personal stories, laughter, and meaningful moments, you will get to know Deborah and Robin while also beginning to explore how understanding trauma can bring clarity to our stories and compassion to the way we relate to ourselves and others.This episode is a mix of celebration, connection, and honest conversation, and it sets the foundation for what is ahead.If you have ever wondered why you react the way you do, or long to understand your story in a deeper way, this episode is for you.We would love to hear your thoughts for future episodes! Email us your ideas at podcast@reclaimedstory.comWant more resources, real stories, and a safe community to grow in your healing?Download the Reclaimed Story app or visit reclaimedstory.com to get connected.Here are two FREE Ebooks for you! 1. Shame Off You: 10 steps to shattering shame in your life, HERE. 2. ABC's: CLICK HERE for a FREE E-book to help you combat lies and replace them with God's truth.  For more encouragement, check out some of our offerings at www.reclaimedstory.comDid you know we have a jewelry line that speaks to your identity in Jesus? CLICK HERE to shop. Every purchase helps support our mission to provide healing and hope to women worldwide. Would you partner with us to spread the message of hope and healing? You can DONATE HERE.  Living the Reclaimed Life is a Reclaimed Story, Inc. podcast, An Arizona non-profit corporation. If you would like to connect with a safe group of women doing real-life together, join our private Facebook page, “Living the Reclaimed Life” or on  Facebook  or Instagram