MaML - Medicine & Machine Learning Podcast

Dr. Max Feinstein is a pediatric cardiac anesthesiologist and notably, a successful YouTuber. He has written on topics like AI's role in future healthcare, vaping's impact on anesthesiology, and recognizing burnout in medicine. Outside of the operating room, he somehow balances fellowship training, teaching, clinical work, and producing popular YouTube videos, demystifying anesthesia. Today we talk about his winding career path, vision for AI in the operating room, and the ethical implications of technology in modern medicine.00:00 - Intro1:16 - From majoring in philosophy to becoming an anesthesia resident 3:33 - Why medicine? Being a wilderness first responder 7:26 - Narrowing down a specialty - anesthesiology vs infectious disease10:17 - What's wrong with infectious disease?11:46 - Why peds cardiac anesthesiology?17:20 - Working and living in a soup kitchen in Colombia22:29 - Who are all these people interested in anesthesia!?27:00 - How Max makes videos31:08 - We already have AI in anesthesia except… 46:56 - Future job market of anesthesia50:36 - Consciousness and anesthesia54:28 - Will AGI really help us?56:53 - Ensuring patient safety in anesthesia58:13 - Could AI make burnout worse?1:02:36 - What gives your life meaning?1:04:51 - Advice for Medical StudentsYouTube - @MaxFeinsteinMDHost: Nathan KellerTwitter: @NathanKellerX Linkedin: https://www.linkedin.com/in/nathankeller1/Producer: Saurin KantesariaLinkedin: https://www.linkedin.com/in/saurin-kantesaria-0a464999

Welcome back David! Dr. David Wu co-founded our podcast back in the height of COVID and is now a radiation oncology resident at Stanford and advisor to an LLM-based startup called Jaide. Since our founding David has been a driving force in communicating the happenings in the medicine and machine learning space and we are excited to talk about his latest efforts in contributing to this area. Jaide uses patient questionnaires to give physicians access to predictive modeling on disease evolution and clinical recommendations based on latest institutional standards to ease documentation and provide world-class care to patients. 00:00:00 - Introduction 00:01:31 - From feeling aimless after undergrad to a Stanford resident and advisor for an AI startup (Jaide)00:05:31 - The beginnings of Jaide: Using LLMs to document patient outcomes 00:07:57 -  LLMs in action - the first clinical trial in Brazil00:11:03 - How do you use Jaide? 00:12:30 - Could LLMs take away key skills from physicians' training?00:15:14 - What if Step 1 was the entry exam to med school? 00:18:10 - What drew you to Stanford?00:21:11 - Have you faced criticism/self doubt about pursuing so many things outside of traditional medicine? 00:23:08 - Why rad onc?00:26:20 - Best/worst med school and residency experiences - “I'm just gonna become a monk and retire” 00:29:33 - Will AI replace doctors? AI isn't even close in this one area.00:35:06 - The double edged sword of AI00:38:41 - How did you choose a specialty? Is the impact of AI important? 00:39:45 - What got you into hip hop/rap? - “Artists make meaning out of suffering” 00:43:45 - What gives your life meaning?00:46:55 - What advice do you have for younger people concerned about the impacts of AI on medicine?Jaide - jaide.careTwitter - @davidjhwu Host: Nathan KellerTwitter: @NathanKellerX Linkedin: https://www.linkedin.com/in/nathankeller1/Producer: Saurin KantesariaLinkedin: Saurin Kantesaria

Dr. Steven Hart is a Senior Associate Consultant in AI at Mayo Clinic who has played a key role in shaping genomics and digital pathology with GenomeGPS, Mayo Clinic's primary DNA sequencing workflow. His groundbreaking contributions have led to advancements in understanding inherited cancer risk and improving digital pathology workflows. With over 100 peer-reviewed publications, Dr. Hart's innovative algorithms are driving efficiency in genetic predisposition testing, reducing unnecessary procedures, and enhancing precision healthcare. We had some audio issues for this one which we tried to fix but they're still pretty apparent so apologies for that D:! 00:00:00 - Introduction  00:01:09 - From a factory worker to a leader in AI and medicine 00:05:11 - Proving people wrong as a motivator 00:06:37 - Crazy factory stories 00:07:38 - Why Mayo Clinic? 00:09:52 - Surprising things about Mayo Clinic 00:11:33 - Is Mayo Clinic's data high quality? 00:12:55 - How to prepare healthcare for AI (and why AI won't actually have the biggest impact) 00:20:50 - Democratizing pathology with AI  00:25:38 - Will AI replace pathologists? 00:29:24 - How do you judge how well an embedding works?  00:33:22 - Reducing expectations for diagnostic AI usage in healthcare 00:36:46 - How do you keep up with the rapidly evolving pace of AI? 00:38:31 - OpenAI o1 and prompt hacking  00:41:27 - Are we close to artificial general intelligence?  00:47:03 - How helpful are regulatory agencies like the FDA with translating AI? 00:49:52 - What makes a good question? 00:53:33 - Favorite parts about living in Rochester, MN 00:55:14 - What gives your life meaning?  00:58:36 - Advice for young people in uncertain times Host: Nathan Keller Twitter: @NathanKellerX Linkedin: https://www.linkedin.com/in/nathankeller1/ Producer: Saurin Kantesaria Linkedin: Saurin Kantesaria --- Support this podcast: https://podcasters.spotify.com/pod/show/maml-podcast/support

Ran Shaul is the chief product officer and co-founder of K Health. With his robust background as a successful founder, Ran has been pivotal in transforming how we approach medical diagnostics and personalized treatment. Under his leadership, K Health has developed innovative AI-driven solutions, including a partnership with Cedars-Sinai and Mayo Clinic. Ran's dedication to improving the patient experience by leveraging technology is reshaping healthcare delivery, making it more efficient and accessible. Hosts: Nathan Keller Twitter: @NathanKell57664 Audio/Video Editor + Art: Saurin Kantesaria Linkedin: Saurin Kantesaria 00:00 - Introduction  00:52 - What are 3 patient questions doctors and AI should help answer?  03:26 - Why does ChatGPT fall short in diagnosing patients?  07:30 - AI does the tedious stuff so doctors can focus on medicine (K Health's model)  09:29 - Combing through 400,000,000 unstructured doctor's notes  11:53 - How do you ask the right clinical questions with AI? 15:41 - Putting a clinician in the loop of AI learning 19:21 - “You can have the perfect algorithm…it does not mean it will be used properly in any clinical setting”  23:27 - The difficulties transitioning from leading a startup to a larger company 26:56 - Telemedicine 2.0 - integrating 24/7 online care with brick and mortar hospitals (Cedars-Sinai Virtual Platform) 31:32 - AI can go further than notes - helping physicians proactively manage patients 39:07 - What gives your life meaning? 42:56 - What advice do you have for young people? --- Support this podcast: https://podcasters.spotify.com/pod/show/maml-podcast/support

Dr. Robert Dürichen leads the machine learning analytics team at Arcturis Data, a company focused on processing and analyzing large-scale electronic health record (EHR) datasets. His current research uses small and large language models to enrich EHR datasets from unstructured patient notes and improve quality through standardization techniques. Hosts: Nathan Keller + Madeline Ahern Twitter: @NathanKell57664 + @maddie_ahern  Audio/Video Editor + Art: Saurin Kantesaria Linkedin: Saurin Kantesaria Intro 0:00 Who is Robert Durichen? 1:29 What is Arcturis? 6:25 How can machine learning speed up clinical trials? 9:43 Typical Arcturis Project 12:06 Progression of Machine Learning 24:45 AI Taking Jobs 29:50 What is Arctex? 33:50 Who works at Arctex? 38:55 Future of Arcturis 40:58 What gives your life meaning? 43:30 Advice for young people on maintaining a work-life balance 44:26 --- Support this podcast: https://podcasters.spotify.com/pod/show/maml-podcast/support

Dr. Nina Kottler is the associate chief medical officer of clinical artificial intelligence and vice president of clinical operations for Radiology Partners, the largest radiology practice in the US, serving over 3,250 hospitals and other healthcare facilities, interpreting over 53 million exams annually.  Host: David Wu Twitter: @davidjhwu Audio Producer: Aaron Schumacher LinkedIn: Aaron Schumacher  Video Editor + Art: Saurin Kantesaria Instagram: saorange314 00:00:58 What brought you to the intersection of medicine and artificial intelligence? 00:07:00 The importance of translating between clinicians and AI engineers 00:12:54 The origins of Radiology Partners  00:16:40 Dr. Kottler's start in Teleradiology  00:21:18 The transition form analog to digital in Radiology  00:27:35 The current state of Radiology Partners 00:32:00 When did Dr. Kottler become a leader in the AI projects? 00:45:00 AI models that Radiology Partners use  00:52:00 Fragility, Technological Evaluation and Business evaluation in Radiology AI systems 00:56:10 Dr. Kottler's thoughts on what the future of AI and Radiology will look like.  01:00:30 Dr. Kottler's advice for people in medicine desiring unique paths.  01:02:45 What brings you joy? --- Support this podcast: https://podcasters.spotify.com/pod/show/maml-podcast/support

Munjal Shah is the co-founder and CEO of Hippocratic AI, a new startup in Generative AI + Healthcare. Hippocratic is building a safety-focused large language model specifically built for the healthcare industry.  Host: David Wu Twitter: @davidjhwu Audio Producer: Aaron Schumacher LinkedIn: Aaron Schumacher  Video Editor + Art: Saurin Kantesaria Instagram: saorange314 Time Stamps: 00:00:58 What brought you to the intersection of medicine and artificial intelligence? 00:06:20 Overview of the American Healthcare System  00:08:06 Hippocratic AI and the Adherence Problem within healthcare  00:14:30 Building an AI Chronic Care Nurse for specific conditions  00:17:15 AI systems and medical co-morbidities  00:24:00 The process of building Hippocratic AI 00:32:45 Becoming more efficient than ChatGPT4 00:33:48 Navigating the problem of hallucinations with Hippocratic AI 00:39:30 How close are we to Health General Intelligence (HGI)? 00:45:40 What advice would you give to someone interested in starting their own company? 00:48:20 How did mentorship shape your path? 00:49:40 What brings you joy? 00:52:25 How do you find novel ideas for start-ups? --- Support this podcast: https://podcasters.spotify.com/pod/show/maml-podcast/support

Dr. Mamdani is a professor, pharmacist, and epidemiologist. He is the Vice President of Data Science and Advanced Analytics at Unity Health Toronto and Director of the University of Toronto Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM). Dr. Mamdani's team bridges advanced analytics including machine learning with clinical and management decision making to improve patient outcomes and hospital efficiency. Dr. Mamdani is also Professor in the Temerty Faculty of Medicine, the Leslie Dan Faculty of Pharmacy, and the Institute of Health Policy, Management and Evaluation of the Dalla Lana School of Public Health at the University of Toronto. He is also a Faculty Affiliate of the Vector Institute. He has published over 500 studies in peer-reviewed journals. Host: Raeesa Kabir  Audio Producer: Melanie Bussan Video Editor + Art: Saurin Kantesaria Instagram: saorange314 Social Media: Nikhil Kapur Time Stamps: 0:00 Dr. Mamdani's Background and Career Path 9:30 Where current data driven medicine strategies fall short and how AI can step in 17:00 How Dr. Mamdani's work in AI and machine learning began 22:00 Applied Health Research Center and the Ontario Policy Research Network 28:45 The impact of utilizing machine learning and AI at the level of patient care - Chart Watch 35:50 Logistics of Developing and Implementing AI solutions 39:10 Insights Gained - From Purpose to Implementation 43:30 Directing Multiple Projects - Recruitment of AI Team  47:45 Future Projects: Back to AI Basics  54:15 Future of AI in Medicine - Fostering trust in AI 57:20 Advice to Younger Self --- Support this podcast: https://podcasters.spotify.com/pod/show/maml-podcast/support

CardinalKit (now Spezi) is an open-source framework for Digital Health Applications and Research. They were recently featured in the news for releasing HealthGPT, an experimental iOS app that lets you query your health data. Spezi is housed in the Stanford Byers Center for Biodesign and directed by Oliver Aalami, MD with Vishnu Ravi, MD as lead architect. Also joining us on this interview is postdoc Paul Schmiedmayer, PhD. Spezi provides a suite of tools to build modern, interoperable digital health tools from the ground up, from the app itself to storing and analyzing collected data in the cloud. It is designed to accelerate rapid prototyping of digital health applications by reducing costs by as much as 75% (~$150,000) and timelines by 12 months. Host: David Wu Twitter: @davidjhwu  Audio Producer + Video Editor + Art: Saurin Kantesaria Instagram: saorange314 Social Media: Nikhil Kapur Time Stamps: 00:58 - The expertise behind Spezi (CardinalKit) 08:03 - Healthcare has a lack of data standardization + Why you should know about HL7 FHIR 14:13 - How did Spezi (CardinalKit) become what it is today? 18:26 - Drink Spezi! 19:53 - Making code/healthcare data more modular and user-friendly 26:40 - Translating a med student's sensor research to a useable device for kids with cerebral palsy 31:20 - From a $40,000 eczema patch test in clinic to a completely at-home test 35:45 - Using healthGPT to make health data easy to understand for patients (LLM on FHIR) 42:35 - How do you deal with privacy issues? 49:33 - What do you think the future of AI in medicine will look like in 10-20 years? 52:00 - Applications where using only an LLM doesn't always work (a case for hybrid systems) 55:30 - What brings you joy? 58:43 - What makes a successful digital health team?

Dereck Paul, MD is a cofounder and the CEO of Glass Health, an AI-powered medical knowledge management and clinical decision-making platform that helps clinicians provide better patient care. Previously, he was an internal medicine resident at Brigham and Women's Hospital, Harvard Medical School and a medical student at the UCSF School of Medicine. Host: David Wu Twitter: @davidjhwu  Audio Producer + Video Editor + Art: Saurin Kantesaria Instagram: saorange314 Social Media: Nikhil Kapur Time Stamps: 01:13 - From music major to med school to making a startup 06:30 - Poor healthcare technology = physician burnout, the motivation for building Glass Health 09:15 - Glass Notebook - "Notion for doctors" 11:24 - Building a startup in the era of Chat-GPT 13:50 - What doctors need in an AI-assisted diagnosis software 19:15 - Transition towards a more AI oriented technology - Glass AI 23:00 - How does Glass AI make accurate diagnoses? 28:40 - Why doctors need to be involved in building clinical AI products 30:50 - Practical usage of Glass AI in the clinic 33:04 - Why Glass AI will be more trustworthy than Chat-GPT in writing clinical notes 37:43 - Why LLMs don't need to be perfect for use in the clinic 40:28 - Ethical implications of Glass AI and similar products 45:34 - Should we disclose when we use AI to write a clinical note? 49:13 - What do you think the future of AI in medicine will look like in 10-20 years? 52:30 - What brings you joy? What gives your life meaning? 56:10 - Would you ever go back to being a musician?

Jerry Liu is the co-founder and creator of LlamaIndex (formerly known as GPT-Index), an interface that allows users to connect their data to LLM's such as Chat-GPT. He has a B.S. in Computer Science from Princeton and has worked at companies such as Quora, Uber, and Robust Intelligence prior to starting LlamaIndex. Host: David Wu Twitter: @davidjhwu Audio Producer: Aaron Schumacher LinkedIn: Aaron Schumacher  Video Editor + Art: Saurin Kantesaria Instagram: saorange314 Social Media: Nikhil Kapur Time Stamps: 01:25 The path to starting LlamaIndex + initial ideas 07:09 LLMs like Chat-GPT vs traditional machine learning 10:00 4 steps of traditional machine learning 10:45 How do large LLMs change the game? 14:11 How does LlamaIndex help LLMs work with unstructured data? 18:08 How do you work with gigabytes of private data? 19:57 Organizing words and paragraphs by topic with embeddings 24:55 The importance of structuring data 26:00 3 key abstractions in LlamaIndex 29:25 Medical use cases for LlamaIndex 31:29 Increasing efficiency in medicine 33:25 An AI medical Research Assistant (Insight) 34:31 Other methods of connecting LLMs to data 36:55 What is langchain? 39:56 What work in the AI and LLM space excites you the most? 42:23 Do you ever feel scared about the developments of AI? 43:45 Llamas and Machine Learning 45:36 What do you think the future of AI in medicine will look like in 10-20 years? 47:24 What advice would you give to grad students, med students, and other early career professionals getting into AI and medicine?

Dr. Ryan earned both a doctorate of medicine (M.D.) and master in public health (M.P.H.) degree from the University of Connecticut in 2001. He completed his postdoctoral training at Harvard's Beth Israel Deaconess Medical Center in Boston, including a chief residency and cardiology fellowship. In 2014 Dr. Ryan started Boards and Beyond, an online lecture library used by medical students across the world to prepare for board exams. In 2022, Dr. Ryan sold his company to McGraw Hill and will continue working to build medical education materials.  Host: David Wu Twitter: @davidjhwu Audio Producer: Aaron Schumacher LinkedIn: Aaron Schumacher  Video Editor + Art: Saurin Kantesaria Instagram: saorange314 Social Media: Nikhil Kapur Time Stamps: 00:55 - How did you come to create Boards and Beyond  08:00 - What was it like to make videos outside of your specialty 09:30 - The launch of Boards and Beyond  12:22 - Designing the Curriculum for Boards and Beyond  15:10 - Jason Ryan on selling Boards and Beyond to McGraw Hill  16:58 - What is next for Jason Ryan? 18:00 - Who were Jason Ryan's favorite teachers  19:48 - What makes a good teacher  23:40 - What are your thoughts on the future of artificial intelligence and medical education 30:03 - Thoughts on Khan Academy's AI-based Khanmigo  31:25 - Jason Ryan's thoughts on becoming a clinician  35:29 - Mentorship throughout Jason Ryan's career  37:35 - Could medical training be shortened? 41:40 - What do you think the future of medicine and artificial intelligence will look like? 43:10 - What advice would you give medical students today? 46:14 - What brings you joy and meaning? What are your greatest fears? 52:48 - What was your lowest point in medical training and how did you overcome it?

Mushtaq Bilal is a postdoctoral researcher at the University of Southern Denmark. He earned his PhD in comparative literature from Binghamton University. He works on simplifying the process of academic writing and writes about ethical use of artificial intelligence for academic purposes. Host: Raeesa Kabir Audio Producer: Melanie Bussan Video Editor + Art: Saurin Kantesaria Instagram: saorange314 Social Media: Nikhil Kapur Music: Caligula - Windows96. Used with Artist's Permission. Introduction and Mushtaq's path: 0:00 seconds  Overview on using AI tools for efficient writing: 8:00 seconds Keeping up to date with all the new apps: 18:00 seconds  Leveling the playing field of academia: 23:15 seconds  Ethical considerations of AI powered writing tool: 40:30 seconds Mushtaq's tutorial for simplifying the academic writing process: 53:20 seconds Fun ending question and ending: 57:30

John Kang, MD, Ph.D. is an assistant professor of Radiation Oncology and Biomedical Informatics Lead at the University of Washington in Seattle. His research interests include the application of Natural Language Processing (NLP) to examine trends in the MaML space. He is a physician-data scientist passionate about uncovering the complex interactions underneath large datasets. He has over 10 years of experience in the novel applications of computational modeling and machine learning in biology systems.  Host: David Wu Twitter: @davidjhwu Audio Producer: Aaron Schumacher Twitter: a_schu95 Video Editor + Art: Saurin Kantesaria Instagram: saorange314 Social Media: Nikhil Kapur 00:45 Could you tell us about your journey to the intersection of medicine and machine learning 07:40 Balancing Residency Training and staying caught up on research in the machine learning space 16:00 Using machine learning to understand biostatistics  18:12 How would you describe the research that you find the most exciting / Unsupervised learning  23:00 Overview of Word Embedding and addressing  potential bias  29:25 Dr. Kang's application of word embedding for research funding  42:52 The intersection of artificial intelligence and human intelligence  45:35 T-SNE / T-Distributed Stochastic Neighbor Embedding in grant analysis  50:50 Has T-SNE helped guide Dr. Kang's research and grant writing  57:00 The future of creativity and ChatGPT  01:02:30 Fear vs Hope in the Medicine and Machine Learning space  01:07:00 What do you think is the future of the MaML space in the next 10-20 years? 01:11:02 What advice would you give yourself as you were finishing medical school?

Welcome back to the third season of the medicine and machine learning podcast! We are kicking off our year with a very unique episode. Our "guest" is ChatGPT! ChatGPT is an artificial-intelligence chatbot developed by OpenAI and launched in November 2022. Since its launch, ChatGPT has been an internet and media sensation. Usage is currently freely available to the public because ChatGPT is in its research and feedback-collection phase. This open interface has been hugely influential in bringing public attention to how AI can be used as a multidisciplinary resource. In this episode, the MaML team asked some fun questions of ChatGPT and gave the answers a voice with text-to-speech software! Don't forget to follow us on twitter @themamlpodcast! contact@themamlpodcast.com Host and Producer: Madeline Ahern / Twitter @maddie_ahern Host: David Wu / Twitter: @davidjhwu Host: Raeesa Kabir Artwork: Saurin Kantesaria Music: Caligula - Windows96. Used with Artist's Permission. 00:40 GPT-4's Intro 01:40 The "Path" of ChatGPT 03:20 ChatGPT's advice for passing STEP exams 07:25 GPT-4 Tackles an Ethics Question 12:00 GPT-4 Tackles a STEP 1 Practice Question 14:44 GPT-4 Tackles a Clinical Scenario 19:02 ChatGPT has passed the boards, how would it do on CASPer? 20:15 The future of AI in medicine 27:31 Closing Remarks

Dr Matthew Lungren is the Chief Medical Information Officer at Nuance Communications, a Microsoft Company. As a physician and clinical machine learning researcher, he maintains a part-time interventional radiology practice at UCSF while also serving as adjunct faculty for other leading academic medical centers including Stanford and Duke. Prior to joining Microsoft, Dr Lungren led the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI). This interview offers great insight for anyone who is interested in non-traditional career paths in medicine at the cutting edge of the MaML space. I hope you all enjoy! and don't forget to follow us on twitter @themamlpodcast! contact@themamlpodcast.com Host: David Wu / Twitter: @davidjhwu Producer: Aaron Schumacher / Twitter: @a_schu95 Artwork & Video: Saurin Kantesaria Music: Caligula - Windows96. Used with Artist's Permission. 00:40 Could you tell us about your path coming to the intersection of medicine and artificial intelligence? 07:00 What literature are you a fan of? 08:50 Where will the next great American Author come from? 14:00 Tell us about your work with Nuance. 17:50 Could you tell us the background of Nuance and Dragon Dictation? 19:55 Tell us about Nuance products that are offered. 23:45 How much code should future physicians know? 28:30 What is a typical day like as chief medical officer 31:30 Do you have any advice for medical students interested in nontraditional career paths? 34:30 How do you balance clinical practice and industry work? 40:40 What are you excited about most in the next 10-20 years? 44:15 How has mentorship shaped your path? 46:08 What advice would you give yourself at your medical school graduation?

Dr. Kaz Nelson is a Fellow of the American Board of Psychiatry and Neurology and serves as Associate Professor in the Department of Psychiatry and Behavioral Sciences and the Associate Designated Institutional Official in the Office of Graduate Medical Education at the University of Minnesota Medical School. She is also host of “The Mind Deconstructed.” In this podcast, Nelson and her brother George dispel myths, address listener questions, and inform the public about mental health and a life worth living. Connect with Dr. Nelson on the following platforms: Twitter: @kazjnelson Facebook: @kazjnelson Youtube: The Mind Deconstructed Host: Madeline Ahern @maddie_ahern Producer: Kirsi Oldenburg Artwork & Video: Saurin Kantesaria Music: Caligula - Windows96, Used with artist permission 01:13 Introduction - Dr. Kaz Nelson 06:20 Sacrifices of Physicians (and their families) 08:25 Limitations of the Mental Health System 13:00 AI Chatbots: Helpful or Harmful? 23:00 High-Acuity Care and Psychiatric Crises 30:30 First Interactions with the Mental Health System 37:10 The Mind Deconstructed 43:45 The Future of AI, Chat GPT 47:30 The Pull of the Status Quo 53:24 Advice to our Listeners

Dr. John Sargent: Dr. John Sargent is the co-founder of BroadReach Group and an internationally recognized thought leader who brings extensive experience in health systems strengthening large-scale patient education programs and the creation and implementation of public-private partnerships in emerging markets. Prior to co-founding BroadReach Group, he obtained his Doctor of Medicine from Harvard Medical School and gained experience as a strategic and operational consultant with expertise spanning multiple disease areas across public and private health sectors. Annika Krugel: Annika Krugel is the Client Director for Vantage Health Technologies, which is a platform from BroadReach that uses AI to aggregate all data in an area or a clinic and then give individualized decision support, operational tools, and step-by-step workflows to empower healthcare workers. Annika has a Master's Degree in Development Studies and has 15 years of experience across civil society and the public- and private sectors in various roles but always in a coordinating capacity. 1:15- Journey to the intersection of medicine and 9:00- Foundation of the BroadReach Group and current work 23:00- Inception of Vantage Health Technologies 25:20- Vantage Health Technologies' role in the pandemic response 35:30- BroadReach Group and Vantage Health Technologies' overall work across the globe 37:00- Future work 45:00- Future of big data and AI in medicine 50:30- Ensuring patient data privacy and security 52:30- Advice and ending thoughts

Dr. Joe Zhang is an Intensive Care doc and health data scientist. He holds a Wellcome Trust fellowship in health informatics and artificial intelligence (AI) at Imperial College London. He has extensive experience in developing and deploying informatics and data solutions in the NHS, and is currently working at the intersection of data science, policy, and infrastructure. contact@themamlpodcast.com Host: David Wu / Twitter: @davidjhwu Producer: Aaron Schumacher / Twitter: @a_schu95 Artwork & Video: Saurin Kantesaria Music: Caligula - Windows96. Used with Artist Permission. 1:10 Your path to the intersection of Medicine and Machine Learning 4:45 What Electronic health records are used in the UK and how does the NHS operate? 12:40 Who uses the data you gather in the NHS? 12:40 Could you tell us about your new publication on the vertical translation of data in AI? 28:00 Can you speak to regulatory bodies and the implementation of AI into healthcare? 32:00 The global clinical AI dashboard 37:25 Any future projects in the pipeline? 39:00 What projects tend to have the most success at being integrated into healthcare? 41:08 How has mentorship shaped your path? 42:15 What do you think the future of AI in medicine will look like in 10 to 20 years? 46:50 What brings you joy and meaning? 48:00 Closing thoughts for the listeners

Ittai Dayan is the co-founder and CEO of Rhino Health, a distributed computing platform leveraging privacy-preserving federated learning. The platform allows medical researchers and healthcare AI developers to seamlessly access diverse and disparate datasets and use them to create better AI algorithms. Host: David Wu / Twitter: @davidjhwu Producer: Aaron Schumacher / Twitter: @a_schu95 Artwork & Video: Saurin Kantesaria Music: Caligula - Windows96. Used with Artist Permission. 00:56 How did you come to the intersection of medicine and artificial intelligence? 06:15 What type of medicine did you start out studying? 11:35 Could you tell us the story behind Rhino Health? 14:30 What is federated learning? 21:00 Common use cases for Rhino Health? 26:45 Relationship between generalizability and accuracy when using federated learning? 28:15 What were your biggest challenges in creating Rhino Health? 32:40 An example of using Rhino Health? 37:40 How does Rhino Health integrate with EHR's 38:15 What are your next steps for Rhino Health? 43:10 What do you think the future of AI in healthcare will look like? 48:08 What gives your life meaning and what are your greatest fears?

Dr. Beth Beadle is the Director of Head & Neck Radiation Oncology at Stanford and co-creator of the Radiation Planning Assistant, a fully automated treatment planning assistant. 00:56 How did you come to the intersection of medicine and AI? 03:20 What is radiation oncology and why does it fit well with AI? 05:54 How has radiation changed in your lifetime? 09:19 What is the Radiation Planning Assistant? 13:10 Describe radiation oncology workflow before the use of the radiation planning assistant 20:45 How does the model compare humans? 23:45 Where was the data source for the radiation Planning Assistant Model? 26:45 What safeguards exist for RPA? 28:00 What has been the response from other physicians, physicists, and patients to this model? 33:30 Timeline for implementation of the radiation planning assistant? 34:45 Background of the Radiation Planning Assistant 38:25 Future of Radiation Planning Assistant implementation 40:55 Adaptive planning in radiation oncology 43:15 Future of the Radiation Planning Assistant in the next 10 years 44:50 Are there any other projects being planned currently? 47:00 How has mentorship shaped your path? 50:25 What is the future of AI in medicine in 10-20 years 51:48 What advice would you tell yourself when you were graduating from medical school 53:20 What brings your life joy and meaning? 54:50 What are your greatest fears? 55:15 Favorite places to travel?

Dr. Tignanelli is the scientific director for the Program for Clinical Artificial Intelligence at the UMN Center for Learning Health Systems Science, the director of UMN Center for Quality Outcomes, and the chair of the Health Information Technology (HIT) Committee for the American College of Surgeons. Dr. Tignanelli's work serving COVID-19 patients during the pandemic and advancing AI research earned him the title of “Health Care Hero” by the Minneapolis/St. Paul Business Journal in 2021. Follow us on Twitter @TheMaMLPodcast Guest: Christopher Tignanelli, @cjtign Host: Madeline Ahern, @maddie_ahern Producer: Kirsi Oldenburg Artwork: Saurin Kantesaria Music: Caligula - Windows96: Used with artist permission Notes: 01:00 tell us about yourself 04:00 critical care/acute care 06:30 COVID acute care 10:00 computer vision 14:00 rib fracture model 15:00 external vs internal validity 19:00 future of AI in medicine

Dr. Chenyang Xu is currently the President and Co-Chairman of PVmed Technologies, Co-Founding Partner of Silicon Valley Future Academy, Managing Partner of Brightway Future Capital, and an Advisory Board Member for the Johns Hopkins University BME department and formerly Advisory Board Member for the UC Berkeley EECS Department. He was formerly the Chief Business Officer of RSP Systems and the GM and CTO of Siemens Technology to Business (TTB) at Berkeley where he led the Siemens's North America's technology startup partnership and early-stage investment practice out of the Silicon Valley. As former head of Siemens Interventional Imaging Program, he led an R&D team that developed over 10 new computer vision-based medical imaging products (e.g. CartoMerge) and has achieved billion dollar scale new revenue stream. Follow us on Twitter! @TheMaMLPodcast Guest: Chenyang Xu Host: David Wu / Twitter: @davidjhwu Producer: Aaron Schumacher / Twitter: @a_schu95 Artwork & Video: Saurin Kantesaria Music: Caligula - Windows96. Used with Artist Permission. 01:00 - tell us about your path 11:00 - the internet + grad school in the 90s 22:00 - “career thinking” advice 30:30 - Siemens VC - looking at 10,000 startups 37:00 - PVMed - AI cancer treatment company in China 43:30 - AI startup ecosystem in China vs. US vs. Europe 56:30 - Potential downsides of implementing AI too quickly? 1:02:10 - Future of AI in medicine in 10-20 yrs? 1:13:00 - “Minority Report”-like AI to predict falls? 1:16:30 - Closing Questions

Steven Lin, M.D. is the service chief of family medicine for Stanford Health Care and the Founder and Executive director for the Stanford Healthcare AI Applied Research Team. Follow us on Twitter! @TheMaMLPodcast Host: Madeline Ahern / Twitter: @maddie_ahern Producer: Kirsi Oldenburg Artwork & Video: Saurin Kantesaria Music: Caligula - Windows96. Used with Artist Permission.

Akilesh Bapu is co-founder and CEO of DeepScribe, an AI-powered medical scribe that passively records and understands a patient's visit, generates a clinical note, and then seamlessly inputs it into the electronic medical record. Follow us on Twitter! @TheMaMLPodcast Guest: Akilesh Bapu / Twitter: @AkileshBapu Host: David Wu / Twitter: @davidjhwu Producer: Aaron Schumacher / Twitter: @a_schu95 Artwork & Video: Saurin Kantesaria Music: Caligula - Windows96. Used with Artist Permission. 1:10 - Pathway towards the intersection of medicine and Machine Learning 9:45 - What specialties are best fitted for DeepScribe implementation 11:55 - How does DeepScribe find relevant information for the clinical encounter 14:45 - The creation process of the DeepScribe hardware. 18:10 - DeepScribe's ability to discern fragments of the SOAP note 19:45 - DeepScribe and billing concerns 24:25 - Scaling the human scribe capacity behind DeepScribe 29:45 - Implementing predictive models with DeepScribe 32:00 - Voice diagnostics 33:40 - Patient privacy and DeepScribe 38:55 - Vision for deep scribe 40:20 - Google Care Studio EHR mention 42:00 - Physician Burnout 42:35 - Advice for future founders 44:15 - What brings you joy? 45:35 - What are your greatest fears? 47:00 - What gives your life meaning? 50:25 - How do medical students think about the future of healthcare

Dr. Nigam Shah is Professor of Medicine (Biomedical Informatics) at Stanford Medicine and Chief Data Scientist at Stanford Healthcare. Dr Shah's research focuses on combining machine learning and prior knowledge in medical ontologies to enable use cases of the learning health system. Follow us on Twitter! @TheMaMLPodcast Guest: Nigam Shah / Twitter: @DrNigam Host: David Wu / Twitter: @davidjhwu Producer: Aaron Schumacher / Twitter: @a_schu95 Artwork & Video: Saurin Kantesaria Music: Caligula - Windows96. Used with Artist Permission. TIMESTAMPS 0:45 tell us about your path and how you came to the intersection of MaML 4:15 what is the thesis of your work? 09:10 where is AI on the gartner hype curve 13:30 the equation of medicine - if risk > threshold, take action. Examples: GreenButton Consultation service + advanced care planning 27:00 how has mentorship shaped your path? 32:00 having fun in projects 34:00 “what makes a project fun?” 37:00 closing questions - what do you expect is the future of AI? 43:00 personal questions - what brings you joy?

Dr. Quynh Nguyen is an assistant professor of epidemiology and biostatistics at the University of Maryland School of Public Health. She received her PhD and MSPH in Epidemiology from University of North Carolina at Chapel Hill, Gillings School of Global Public Health. Dr. Nguyen is a social epidemiologist focusing on contextual and economic factors as they relate to health. She joined us to talk about her projects that leverage technology and big data sources to investigate and address health disparities. Host: Raeesa Kabir Producer: Kirsi Oldenburg Artwork: Saurin Kantesaria Follow us on Twitter: @TheMaMLPodcast Have a speaker you would like to see on our podcast? Contact us at contact@themamlpodcast.com

Dr. Vineeta Agarwala is a general partner at a16z, physician, adjunct clinical professor at Stanford, previously at Google Ventures, Flatiron Health, having received her MD/PhD at Harvard Medical School and the Broad Institute. Many thanks to Ashlea Kosikowski from 1AB Media for making this episode happen! Follow us on twitter: @TheMaMLPodcast Host: David JH Wu @davidjhwu Producer: Aaron Schumacher @a_schu95 Design: Saurin Kantesaria

Dr. Harald Kittler is a Professor in the Department of Dermatology at the Medical University of Vienna, in Vienna, Austria. Dr. Kittler is the founder of Dermachallenge, a 2018 startup which uses the principles of gamification to teach and train health professionals. As a dermatologist and dermatopathologist, Dr. Kittler has a unique insight into how future physicians might be trained in his profession. Enjoy some gamification of dermatology by joining the over 5,000 players at Dermachallenge! Host: Madeline Ahern Producer: Kirsi Oldenburg Artwork & Video: Saurin Kantesaria Music: Caligula - Windows96 (Used with artist permission) Have a speaker you would like to see on our podcast? Contact us at contact@themamlpodcast.com

Pat Walters is the Chief Data Officer and Patrick Riley is the senior VP of AI from Relay Therapeutics in Cambridge, MA. In this episode we discuss Relay's innovative approach to drug discovery, and how new developments in AI and computational modeling have accelerated this process.

Matt Diamond, MD, PhD, is Chief Medical Director of the FDA's Digital Health Center of Excellence. Dr. Diamond provides leadership for digital health policy development and implementation for emerging technologies including artificial intelligence. Prior to joining the Agency, Dr. Diamond served on leadership teams of large and small technology companies, including as Chief Medical Officer at Nokia, and as Medical Director at Fossil Group. He earned his MD and PhD (biophysics) from the Mount Sinai School of Medicine, and is board certified in rehabilitation medicine and sports medicine and certified in medical acupuncture. TIMESTAMPS 1:15 tell us about your path 5:15 what's a typical day like 8:26 how would you define a digital health technology? 10:50 regulation of medical devices is based on their intended use 13:00 are AI technologies fundamentally different from a stethoscope 19:30 talking about new FDA guidelines - Digital Health Technologies for Remote Data Acquisition in Clinical Investigations 27:00 collaborative communities (join one!) 30:00 FDA & relationships with companies 37:00 what do you think the future of AI in medicine will look like? 39:55 what advice would you give yourself Contact us! contact@themamlpodcast.com Host: David Wu / Twitter: @davidjhwu Producer: Aaron Schumacher / Twitter: @a_schu95 Artwork & Video: Saurin Kantesaria Music: Caligula - Windows96. Used with Artist Permission.

Joy Kincaid is our guest today from OncoHealth, which has just launched their new digital telehealth platform supporting cancer patients and their families. Joy was formerly VP of Population Health at Optum.

Description: Margaret Elizabeth Ross, M.D., Ph.D. is a Nathan Cummings professor in neurology and director of the Center for Neurogenetics at the Feil Family Brain and Mind Research Institute at Weill Cornell Medicine in New York. To learn more, check out the Ross Lab website! 1:30 Introduction 4:00 An Intro to AI 10:15 What are Neural Tube Defects? 16:30 Publications from the Ross Lab 21:00 The Ross Lab's Use of AI 30:00 Clinical Applications of Genetic Research 31:00 Spina Bifida Outcomes 32:00 Health Equity in Genetics 36:40 What's Next? 42:00 What's the future of AI in Medicine? 43:40 Advice for your Past Self 45:00 Advice for Medical Student/Physicians 48:00 Final Words of Wisdom Intro Music - Windows96 - Caligula (song used with permission from artist). Host: Madeline Ahern Producer: Melanie Bussan Cover Art: Saurin Kantesaria Follow us on twitter @themamlpodcast Email us! contact@themamlpodcast.com Looking for industry sponsors!

Description: Neal Khosla is the founder and CEO of Curai Health, a digital health startup making big waves in the primary care space. To learn more, please visit curaihealth.com. 1:00 Tell us about your path and how you came to the intersection of medicine and machine learning 13:15 - Most common patient demographic served 15:45 - Story of how Curai began. + history of medicine 29:00 - Can med + AI reason better than us? + vision for Curai 40:30 - When should patients go into clinic? 44:40 How has regulation affected you 47:00 - How has mentorship shaped your path 53:20 - 3 questions i want to ask everyone What are you most afraid of? What do you believe in? What gives you strength? 58:00- What do you want from the universe? Intro Music - Windows96 - Caligula (song used with permission from artist). Host: David Wu @davidjhwu Producer: Aaron Schumacher @a_schu95 Cover Art: Saurin Kantesaria Follow us on twitter @themamlpodcast Email us! contact@themamlpodcast.com Looking for industry sponsors!

"Because Life needs Art to explicate its meaning, and Art needs Technology to keep its edge bleeding."

Yvonne Lui, MD, is the associate chair of radiology of NYU Langone Health. Today we discuss FastMRI, the interesting collaboration between NYU and Facebook AI. We explore how machine learning can enhance image reconstruction following an MRI scan. 0:00 Background on MRI 2:36 Intersection of Medicine and Machine Learning for Dr. Lui 6:40 Current Research Projects 11:40 MRI partnership with Facebook AI & MRI Image reconstruction 17:12 Models for research in clinical trials 21:30 Why Facebook is interested in this problem 24:15 Further information on the Machine Learning MRI reconstruction 27:45 Academic & Industry Collaboration 35:00 Hesitancy in collaborating with industry 37:05 What areas of AI are Dr. Lui interested in 38:45 Future of AI and Medicine 40:30 Radiology and Automation 43:00 Dr. Lui's balance between clinical work, research, and administrative work 48:20 What advice would you give to yourself early on in your career. Host: David Wu @davidjhwu Producer: Aaron Schumacher @a_schu95 Cover Art: Saurin Kantesaria

David Lindsay is the CEO of Oncora, a data, documentation, and personalized care solution for specialty oncology. Oncora is currently implemented in many major hospital systems, including Northwell Health, MD Anderson, Mass General Children's, and Scripps Health. David founded the company during his MD/Ph.D. training at the University of Pennsylvania. 0:00 Introduction 1:15 David Lindsay tells his story 5:00 Initial Idea for a company bringing AI to oncology 7:35 When did David decide to start the company 10:25 Balancing being a CEO and a medical student at the same time 12:30 The early challenges in starting the company 14:30 The initial offerings of the company 17:50 Obtaining initial data sets 20:25 Initial funding for the company 24:45 A use case to predict hospitalization 27:10 How are these AI technologies regulated 30:00 How do clients pay for this technology 32:45 Tracking quality metrics 36:00 Integrating with an EHR 37:30 Further development of the product 41:35 Next steps for Oncora 43:55 Industry vs Academia 47:30 Next for AI in 10 - 20 years 48:20 Advice for yourself 20 years ago 50:00 Advice for students in industry 53:30 David's plans for the future Host: David Wu @davidjhwu Producer: Aaron Schumacher @a_schu95 Cover Art: Saurin Kantesaria

Carmen Aguirre is a 4th-year medical student, visual jockey (VJ), and NFT artist. Here we sit down and discover what pushes her to continue developing her passions for medicine and artificial intelligence. You can find more info about her work here: https://linktr.ee/Neurite 0:00 Alex gives some background on NFT's 2:50 Background and Initial Involvement with NFT's 5:50 Seeing stigma around mental health conditions 9:10 Building a community around NFT's 9:40 The process of creating NFT's 13:20 Working for Ariana Grande 15:10 Working as a DJ and in music while in medical school 21:10 "Work is my life" - how Carmen balances the commitments 22:10 Time management 24:35 How a creative outlet benefited Carmen's mental health 27:25 Raising Money for mental health charities through NFT's 30:30 Going from animation to the drop process 34:00 Return of live music, graduating medical school and residency 36:45 The average medical student compared to the average 24-year old musician 39:15 How will technology change medicine in 10-20 years 41:00 Advice you would have given yourself going into your 20's Interviewer: David Wu Producer: Aaron Schumacher Art: Saurin Kantesaria

In our first ever two-interviewee episode we welcome Doctors Shannon Haymond and Christopher McCudden. Dr. Haymond is the Vice Chair for Computational Pathology and Director of Mass Spectrometry at Lurie Children's hospital of Chicago and an Associate Professor of Pathology at Northwestern University Feinberg School of Medicine. Dr. McCudden is the Vice Chair for the Department of Pathology & Laboratory Medicine at the University of Ottawa and a Clinical Biochemist in the Division of Biochemistry at The Ottawa Hospital. They are co-authors of "Rise of the Machines: Artificial Intelligence and the Clinical Laboratory." This article details the potential uses for artificial intelligence within the clinical laboratory. From newborn screening and inborn errors of metabolism to toxicology screens and everything in between, this article not only provides insight into the future of artificial intelligence, but also a peek into the clinical laboratory. In this interview, we talk about the many paths to understanding and working with machine learning, from Dr. Haymond who is classically trained, to Dr. McCudden, who taught himself "R." We discuss the use of mass spectrometry, genomics screenings, and other current laboratory techniques and how they might be aided by artificial intelligence. We hope that this interview gives you a comprehensive look into the world of laboratory medicine that is at the heart of all healthcare systems. Thank you and enjoy! 1:37 What are Clinical Laboratories? 3:20 Breaking up with Excel - Dr. Haymond's Journey 5:49 All Specialties use the Lab 7:40 Learning “R” - Dr. McCudden's Journey 11:31 Clinical Mass Spectrometry 15:45 Newborn Screening and Genetic Testing 17:41 Clinical Biochemistry 21:56 Artificial General, Narrow, and Super Intelligence 23:40 AI in Genomics 29:55 Open Source or Proprietary? 32:33 Future of AI In the Clinical Lab 38:50 Advice to Listeners

Shanen Boettcher is a former general manager at Microsoft, product manager at Netscape, and now a PhD student at the University of Saint Andrews currently studying AI Ethics and Spirituality. Shanen was recently featured in a New York Times Article titled, "Can Silicon Valley Find God?" He is a pioneer in this field and deftly explores two very disparate topics to deeper probe the pressing questions of our generation. In this interview we discuss how artificial intelligence can facilitate positive conversations about faith, the impact of faith/spirituality on health, as well as other interesting topics like the study of world religions and spiritual texts. We close with some advice for individuals looking to get involved in this sort of work. 1:20 Introduction and Background 2:55 Why study world religions? 5:50 Looking back on the days at Microsoft 7:20 The research that led to a New York Times article 11:55 Should AI expose people to their existing religious beliefs or provide new perspectives from other religions. 17:00 Where does this research go from here? 20:10 Do interactions with AI have an impact on people's religious beliefs? 27:18 Use of algorithms for answering existential questions 30:00 How can this research help people? 37:10 Preventing religious bias in AI systems 40:20 Will technology bring us closer or further from our spirituality? 49:55 What should AI say when you ask existential questions? 52:55 Does the source of the voice affect an individual's interpretation of the answer? 1:02:50 Advice for those in their 20's 1:07:30 Do we need religion going forward? 1:13:45 A favorite spiritual text

Dr. Jakub Tolar is the Dean of the University of Minnesota Medical School and is a Distinguished McKnight Professor in the Department of Pediatrics, Blood and Marrow Transplant & Cellular Therapy. He is the Vice President for Clinical Affairs at the University of Minnesota, Board Chair for University of Minnesota Physicians and co-leader of M Health Fairview. We have come to know him not only as a researcher and dean, but as a passionate advocate who is putting artificial intelligence at the forefront of academic medicine. 1:00 MaML @ UMN 2:08 Tools to Alleviate Human Suffering 4:00 The Brain Machine 6:56 How do we know things are real? 9:00 Serving Minnesotans 10:19 Meet the Dean 16:48 Rare Genetic Disorders and ML 18:14 Mori et al. Article (see citation) 19:00 Medical Errors 20:45 AI in Medical Education (see citation) 24:25 Mistakes of Modern Living 24:50 Antiquity and Modernity 30:35 Data Ownership  32:38 The EHR Conundrum 37:29 Technological Liberation 39:15 Epidermolysis bullosa 47:23 Dean Tolar's Advice 51:22 Future of AI in Medicine 54:50 Make Journaling a Part of Your Day! Mori, J., Kaji, S., Kawai, H. et al. Assessment of dysplasia in bone marrow smear with convolutional neural network. Sci Rep 10, 14734 (2020). https://doi.org/10.1038/s41598-020-71752-x Lentz A, Siy JO, Carraccio C. AI-ssessment: Towards Assessment As a Sociotechnical System for Learning. Acad Med. 2021;96(7S):S87-S88. doi:10.1097/ACM.0000000000004104 Interviewer: Madeline Ahern Producer: Melanie Bussan Art: Melanie Bussan Follow us on Twitter: https://twitter.com/TheMaMLPodcast?s=20

Dr. James Zou of Stanford University is an inaugural Chan-Zuckerberg investigator and faculty director for the university-wide AI for Health program. Dr. Zou recently published a paper in Nature which is making waves in the clinical trial world because it is causing us to rethink how we set eligibility criteria for clinical trials. Using an ML approach, he shows that by changing such criteria, we can make trials both more inclusive, opening them up to way more patients, while at the same time safeguarding patient safety. We also talk about his various other research projects, which span the gamut from evaluating FDA approvals of AI algorithms, all the way to deeper mathematical concepts like data valuation. Dr Zou is an impressive titan in the AI and medicine space. In this interview I really came to appreciate how broad his research spans, which I think is key to his many successful projects. We ultimately close with some good advice for people looking to get involved in this exciting and growing space. 02:45 Introduction to the intersection of Medicine and AI 4:20 Life after Ph.D. 6:35 New Nature paper on AI and clinical trials 13:25 How did we approach this question? 14:15 Data Driven Approach - Trial Path Finder 16:59 The ethical implications of this approach 19:35 Why are minority populations excluded from research? 20:25 Using AI to include ineligible patients in clinical trials 23:50 Future for this project 27:00 Evaluation of FDA approvals for AI algorithms 32:23 Favorite Project Dr. Zou has worked on 35:01 Dr. Zou's favorite math concept in the machine learning space 39:10 Separating signal from noise 39:53 Dream research projects 41:00 Future of Ai % medicine in 10-20 years 43:15 The human and AI team 47:40 What advice would you give to your 20 year old self Interviewer: David Wu Producer: Aaron Schumacher & Alexander Jacobs Art: Melanie Bussan Follow us on Twitter: https://twitter.com/TheMaMLPodcast?s=20

In this episode we discuss a novel idea in the healthcare and AI space: using Swarm Learning and blockchain technology for decentralized and confidential machine learning on clinical data. This promising new framework for collaborative research improves both algorithm performance and preserves patient privacy. This idea has been pioneered by Dr. Joachim Schultze, who recently published an exciting new paper on the subject in Nature. Dr. Joachim Schultze is a professor of Genomics and Immunoregulation at the DZNE in Germany and the University of Bonn. 2:30 Introduction and Background 6:00 Studying Broadly as an Academic 9:10 Joachim Schultze's introduction to A.I. through work on Leukemia 14:45 Recent Nature Paper - Swarm Learning & The Blockchain 21:10 Federated Learning vs. Swarm Learning 23:00 Using the Blockchain and Smart Contracts to Secure Data Sets 25:00 External Threats to the Swarm 29:40 Reaching Agreement Before Inter-Institutional Swarm Learning 35:50 Utilizing Multiple Nodes to Answer a Clinical Question 39:18 Reducing Technology-Driven Noise and Decreasing Bias With the Swarm 44:50 Open Science, Open Insights, But is Open Data Absolutely Necessary? 46:46 The Necessity of an Interprofessional Team to Complete This Project 48:30 Next Steps For This Project 54:10 Central Maintenance For The Swarm 55:10 Future of A.I. in Medicine 59:40 What Advice Would You Give To Your 20-Year Old Self Interviewer: David Wu Producer: Aaron Schumacher & Alexander Jacobs Art: Saurin Kantesaria @saorange314 - Instagram

Professor Glenn Cohen is a James A. Attwood and Leslie Williams Professor of Law at Harvard University. Professor Cohen is one of the world's leading experts on the intersection of bioethics and the law and is the author of more than 150 articles appearing in such places as New England Journal of Medicine, JAMA, The American Journal of Bioethics, The New York Times, and The Washington Post. He also leads the Project on Precision Medicine, Artificial Intelligence, and the Law, which is part of the larger Centre for Advanced Studies in Biomedical Innovation Law. In this interview, we discuss a variety of legal and ethical topics like data privacy, liability and medical errors, and AI use disclosure in patient settings. Professor Cohen provides many examples of how AI is changing the face of our society from driverless cars to Target knowing us better than our own family members! He also makes a few great literature and media recommendations: "Exhalation" by Ted Chiang, "The Paper Menagerie" by Ken Liu, "The Three-Body Problem" by Liu Cixin, and of course, the Netflix original, "Black Mirror." P.S. Follow professor Cohen on Twitter (@CohenProf) for more nuggets of wisdom on legal and ethical issues in artificial intelligence (and in many other healthcare sectors)! 1:30 Professor Cohen's Journey 3:17 Project on Precision Medicine (PMAIL) 5:46 "Case-based" approach 8:57 Who takes the blame? 11:20 Driverless cars and healthcare 12:33 Medical errors 13:08 Big data, HIPPA 16:30 Where are we going? 18:40 Bias in AI + Healthcare 20:00 Advice to your past self! 22:30 Vital interprofessional collaboration Interviewer: Madeline Ahern Producer: Melanie Bussan Art: Saurin Kantesaria @saorange314 - Instagram

Dr. Vivian Lee, MD, Ph.D., MBA is currently President of Health Platforms at Verily, an Alphabet Company. Dr. Vivian Lee is also the author of the latest book “The Long Fix,” a book about solving America's healthcare crisis. Dr. Lee has accomplished much in her diverse career. She received a doctorate in medical engineering from Oxford as a Rhodes scholar, her MD from Harvard Medical School, was valedictorian at NYU Stern School of Business, authored over 200 peer-reviewed research publications, as well as a cardiovascular MRI textbook, former CEO of the University of Utah Health and dean of their medical school and, is now the President of health platforms at Verily Life sciences, an Alphabet company. In this interview, we talk about her journey to Verily today and her thoughts on how healthcare has been changed for the better by new technologies like Digital Health Platforms, an example being Onduo for blood glucose management in diabetics. We also talk about how medicine has changed from the days she started medical school to the future landscape that current medical students face today, one that is much more integrated with payers, tech, politics, and employers. We hope that this interview inspires you as it did to us to try and tackle all of healthcare's problems with renewed vigor. Thank you and enjoy! P.S. Please check out Dr. Vivian Lee's latest book “The Long Fix” and review it on Amazon/GoodReads!! 2:50 Dr. Vivian Lee's journey 8:20 Transition to Radiology 12:40 Transition to Univ. of Utah 15:50 “What does your job at Verily entail?” 17:10 Onduo - an example of Health Platforms in action 23:50 Verily and COVID testing 27:40 “The Long Fix” and the Co-Production of Health 37:00 MedSchool now vs. MedSchool then 44:00 Verily and how it affects the future of medicine 46:00 David's misattributed Luddite fears 50:00 What advice would you give your younger self? Interviewer: David Wu @davidjhwu - Twitter Producer: Aaron Schumacher @a_schu95 - Twitter Art: Saurin Kantesaria @@saorange314 - Instagram

Dr. Faisal Mahmood is an Assistant Professor of Pathology at Harvard Medical School and Computational Pathology at Brigham and Women's Hospital. Dr. Mahmood recently published an exciting new paper this year where he and his team built a deep learning model to accurately identify tumors of unknown origin on pathological slides (Lu et al., Nature 2021) Pathology is one of the central pillars of medicine and here we really dive deep into how machine learning is pushing the boundaries of the field and our abilities to diagnose and recognize tumors. Enjoy! Twitter: @TheMaMLPodcast Interviewer: David JH Wu (@davidjhwu) Producer: Aaron Schumacher (@a_schu95) Cover Art: Saurin Kantesaria 1:20 Background in computational pathology 5:30 Interest in Pathology 10:20 Modern algorithms detecting biomarkers to better educate physicians 12:05 Using AI to identify tumors of unknown origin 17:10 Building the TOAD AI Model 21:50 Assessing the validity of the Toad Model 23:30 Determining inputs for the TOAD Model 26:30 Diversity with the TOAD Algorithm 27:40 Next steps for the project 33:15 Using AI to augment physicians' abilities 35:06 Advice for physicians interest in AI 37:00 Dream Research Project 38:40 Will AI make medical discoveries in the future? 40:38 Advice you would give to yourself in your 20's 41:55 Obtaining a Ph.D. in Japan 44:00 Closing thoughts Paper: Lu et. al, “AI-based pathology predicts origins for cancers of unknown primary.” Nature, 2021

On this month's episode of the MaML podcast we interview Dr. Nneka Comfere, a Dermatologist and Dermopathologist at the Mayo Clinic in Rochester, MN. We begin by discussing Dr. Comfere's discovery of visual beauty within dermatology and how this can be applicable in a machine learning setting. We also talk about the possible uses for dermatoscopes and artificial intelligence to fill gaps in care based on location. Dr. Comfere's take on AI from a clinician's perspective is accessible to not only medical professionals, but also those seeking to learn more about how machines are becoming part of the healthcare system. Enjoy! 0:25 - Journey to Dermatology and Dermopathology, Integration of AI 9:35 - Articles in Journal the American Academy of Dermatology 19:25 - Initial Venture into AI, Building a Dermatological Database 28:32 - Future of AI in Medicine 32:15 - What is Next for Dr. Comfere? 36:51 - Advice for Students/Learners

In this episode of the Medicine & Machine Learning podcast we talk to Dr. Ian Pan, a Kaggle Grandmaster, a soon-to-be radiologist resident at Brigham and Women’s Hospital, and a rising star in the medicine and AI field. Ian is a very talented coder who has won numerous Kaggle competitions, which are international data science competitions that are both very competitive and prestigious. Ian is fresh out of medical school and currently wrapping up his intern year at University Hospital in Cleveland. We talk about his path to medicine and AI as well as the various strategies he’s used to become number one in this burgeoning new field. Ian also gives some great advice on how to get started and we close with some of his exhortations against poor practices in ML today. This interview was a lot of fun and if you are curious about Kaggle competitions or how to be the best at them, this interview is for you. Time-Stamps 6:20 Initial interests in Radiology 12:50 2018 Pneumonia detection Kaggle challenge 17:55 Domain expertise not necessary for AI learning 20:18 How to approach an AI challenge 25:23 The structure of Ian's Kaggle-winning models 28:58 What sets Ian's models apart 32:30 Non-medicine endeavors 37:40 Coding Background 39:00 Should medical students learn to code 42:00 The future of AI in medicine 49:10 What’s next for Ian 54:07 Necessary changes to AI in medicine 57:15 Advice for medical students

In this episode, we interview Dr. Anouk Stein, of MD.ai, a healthcare start-up based in NY. We discuss Dr. Stein's journey to MD.ai as well as her current work in the medical AI space. Dr. Stein provides some great advice for anyone looking to get started in practical machine learning. We also talk about some of the exciting kaggle competitions held by MD.ai as well as the importance of external data validation. We close with some great advice for our listeners from Dr. Stein on how to embrace the exciting new changes taking place in Medicine today. Dr. Stein is a terrific teacher and I learned a lot from her in this interview. I hope you all enjoy! Timestamps 1:10 Individual path to healthcare and AI 3:58 What does MD.ai do? 5:00 Stanford Design-Your-Life course 7:40 AI and Radiologists 12:00 Combining algorithms and the necessity of a meta-algorithm 14:48 External Validation of Data 16:50 Practical Machine Learning 22:10 What is external validation 25:10 Controversy over generalization 29.10 Accomplishments of MD.ai 32:30 What is it like to work in industry after medical education 37:45 How MD.AI got started 40:25 Fields of Medicine looking towards AI 43:40 The future of AI in medicine over the next 10 - 20 years 46:25 What advice would you give to yourself in your twenties 47:00 Any advice for young physicians Resources Mentioned: Kaggle - Python tutorial Fast.ai Facebook Detectron 2 Pandas

On today’s MaML Podcast we interview Dr. Judy Gichoya, an interventional radiologist at Emory University in Atlanta. We begin by talking about Dr Gichoya’s early days in Kenya where she participated in building OPEN MRS, the world’s leading open-source EMR platform. We then talk about her work in using AI to combat bias and social injustices in medicine and the importance of diversifying the datasets we use in AI work today. 03:00 Origins in Kenya, building OpenMRS, path to AI 14:00 Research topics of interest in the Gichoya Lab (Emory) such as bias in AI 21:00 steps we can take to combat bias in datasets 27:00 work on federated learning 36:00 advice to medical students / early-career med students interested in the field 43:00 balancing clinical work and informatics research 50:00 favorite food from hometown!

Welcome to the Medicine and Machine Learning Podcast, brought to you by the students of the University of Minnesota medical school. Today’s guest is Dr. Daniel Tse, MD, a product manager at Google. Dr. Tse’s path to healthcare and AI is an unconventional one, and in today’s episode, in addition to talking about his path to Google after medical school, we talk about something that I think anyone in any career will find relevant, and that is the doubt that comes when you wonder if the path you are on is the right path. I can’t say that we’ve discovered the perfect answer in this interview, but I will say that Dr. Tse is someone who has thought deeply about this question as you can see from his unique career and will probably provide some good words of advice. Had a real pleasure recording this interview. Hope you all enjoy! 1:30 Childhood + Budding Interest in Computers 8:00 Path to medicine 12:00 Working at a healthcare startup during medical school 20:00 Working at Google after graduating medical school 30:00 Comparing and contrasting Google with an academic research lab 35:00 MD training + how it relates to working at Google 38:00 What does a product manager do? 45:00 What do you expect is the future of AI + medicine? 49:00 What advice would you give to your 25 yr old self? 52:00 Advice to people who are questioning whether medicine is the right path for them