#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.

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What We Cover:

• Why AI should learn from patients and outcomes, not just doctors
• How an ECG model found hidden risk of sudden cardiac death
• Why medical data access is still a major bottleneck
• How more measurement could actually mean fewer unnecessary tests
• Why 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 AI
• 05:54 The problem: There’s no variable called “get sick”
• 09:27 Algorithms optimize exactly what you tell them to
• 23:03 Why teaching AI to copy doctors limits what it can discover
• 26:28 How AI could create a new science of medicine
• 28:40 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 data
• 49:00 Healthcare’s “mainframe to PC” transition
• 55:08 Why more measurement could actually mean fewer tests
• 58:46 Why medical AI is aiming too low
• 1:02:13 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.aax2342
• An Algorithmic Approach to Reducing Unexplained Pain Disparities in Underserved Populations (Nature Medicine, 2021)https://www.nature.com/articles/s41591-020-01192-7
• An ECG Biomarker for Sudden Cardiac Death Discovered with Deep Learning (Nature, 2026)https://www.nature.com/articles/s41586-026-10674-6
• Predicting the Future — Big Data, Machine Learning, and Clinical Medicine (NEJM, 2016)https://www.nejm.org/doi/full/10.1056/NEJMp1606181
• Nightingale Open Sciencehttps://www.nightingalescience.org/
• Dandelion Healthhttps://dandelionhealth.ai/

Watch the conversation: https://youtu.be/WDo-iL6O5nU

Find 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.