Medical AI is full of impressive models that work on retrospective datasets. The much harder problem is turning one into a tool that can operate continuously inside a hospital, earn clinicians’ trust, fit into an overloaded workflow, stay reliable across different health systems, and ultimately improve patient outcomes.
Mike Haney talks with Johns Hopkins researcher and Bayesian Health founder Dr. Suchi Saria about the decade-long journey from an academic sepsis-prediction algorithm to AI that clinicians are using in hospitals today. Her experience is a reality check on what it actually takes to make medical AI useful, and an argument for aiming higher than documentation and billing: toward systems that continuously watch patients, catch deterioration earlier, and help prevent avoidable harm.
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What We Cover:
🎙️ About the Guest:
Dr. Suchi Saria is the John C. Malone Associate Professor at Johns Hopkins University, where she directs the Machine Learning and Healthcare Lab, and is the founder and CEO of Bayesian Health. Her work focuses on building and deploying machine-learning systems that use real-world clinical data to improve diagnosis, treatment, and patient safety.
📍What Dr. Suchi Saria & Mike Haney discussed:
🔗 Helpful Links:
Watch the conversation: https://youtu.be/uVEKgSvei4s
Find us on YouTube: https://youtube.com/levelshealth?sub_confirmation=1
👋 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.