#309 - What AI Sees That Doctors Can’t | Dr. Ziad Obermeyer & Mike Haney

Medical AI is already getting good at doing things doctors do. Dr. Ziad Obermeyer thinks the bigger opportunity is using AI to discover things medicine doesn’t yet know.His work shows both sides of that future: algorithms can amplify bad assumptions when trained on the wrong targets, but they can also uncover signals in medical data that humans miss. Obermeyer argues that as more health data is collected outside the hospital, AI could turn it into a continuous picture of health rather than a series of isolated snapshots.Free course: Improve your metabolic healthGet our free email course on how glucose, nutrition, exercise, sleep, and measurement can help you build habits that support better energy and long-term health: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://levels.link/wnl⁠What We Cover:Why AI should learn from patients and outcomes, not just doctorsHow an ECG model found hidden risk of sudden cardiac deathWhy medical data access is still a major bottleneckHow more measurement could actually mean fewer unnecessary testsWhy healthcare may be entering a “mainframe to PC” transition🎙️ About the Guest:Dr. Ziad Obermeyer is an emergency medicine physician and an Associate Professor at the UC Berkeley School of Public Health. He also co-founded Dandelion Health and the non-profit Nightingale Open Science. 📍What Dr. Ziad Obermeyer & Mike Haney discussed:02:43 Why better data is fundamental to medical AI05:54 The problem: There’s no variable called “get sick”09:27 Algorithms optimize exactly what you tell them to23:03 Why teaching AI to copy doctors limits what it can discover26:28 How AI could create a new science of medicine28:40 Could AI predict sudden cardiac death before it happens?34:14 Can an algorithm teach us what it sees?36:50 Medicine’s biggest AI bottleneck: access to data49:00 Healthcare’s “mainframe to PC” transition55:08 Why more measurement could actually mean fewer tests58:46 Why medical AI is aiming too low1:02:13 What the next generation of wearables needs to measure🔗 Helpful Links:Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations (Science, 2019)https://www.science.org/doi/10.1126/science.aax2342An Algorithmic Approach to Reducing Unexplained Pain Disparities in Underserved Populations (Nature Medicine, 2021)https://www.nature.com/articles/s41591-020-01192-7An ECG Biomarker for Sudden Cardiac Death Discovered with Deep Learning (Nature, 2026)https://www.nature.com/articles/s41586-026-10674-6Predicting the Future — Big Data, Machine Learning, and Clinical Medicine (NEJM, 2016)https://www.nejm.org/doi/full/10.1056/NEJMp1606181Nightingale Open Sciencehttps://www.nightingalescience.org/Dandelion Healthhttps://dandelionhealth.ai/Watch the conversation: https://youtu.be/WDo-iL6O5nUFind us on YouTube: ⁠⁠⁠⁠⁠⁠⁠https://youtube.com/levelshealth?sub_confirmation=1⁠⁠⁠⁠⁠⁠📲 Connect:Connect with Dr. Ziad Obermeyer on https://ziadobermeyer.com/👋 Who we are:Levels helps you understand your metabolic health with personalized data, expert guidance, and tools that connect your daily choices to measurable changes in your body. Our goal is to help you make better decisions about food, exercise, sleep, and long-term health.Look for new shows every month on A Whole New Level, where we have in-depth conversations with thought leaders about metabolic health.