AI and Clinical Practice—Discovery and Scaling Findings From Large, Multicenter Health Care Datasets

How can we leverage AI to transform health care into a more efficient model for delivering care? In this Q&A, JAMA Editor in Chief Kirsten Bibbins-Domingo, PhD, MD, MAS, interviews Atul Butte, MD, PhD, the director of the Bakar Computational Health Sciences Institute at UCSF, to discuss scalable privilege and the need for the broad distribution of AI-driven expertise. Click https://ja.ma/3u1D7Ey to read “Scalable Privilege”—How AI Could Turn Data From the Best Medical Systems Into Better Care for All in JAMA. Topics discussed in this interview: 00:00 Introductions 01:34 Data fuels artificial intelligence. 03:38 What does it mean to think across many different health systems? 04:30 Care Patterns and Pathways of Patients 06:58 Patient Concerns About Their Data 08:15 The Use of De-identifiable Data vs Identifiable Data 09:23 Reasons why patients would want a health system to have their data 10:46 Random Control Trials (RCTs) vs Real World Evidence 12:25 How to evaluate studies with real world data? 13:55 AI tools need lots of data to learn. 15:41 Scalable Privilege 19:20 How do you use AI tools in your daily work? 20:43 What types of AI tools are you skeptical of? 21:49 Conclusion --------------------------------------------------------------------------------- For more from JAMA • https://www.jama.com •   / jamajournal   •   / jama_current   •   / jama.  . Follow the JAMA Network • https://www.jamanetwork.com • http://www.jamanetworkaudio.com •   / jamanetwork   •   / jamanetwork   •   / jamanetwork   •   / jamanetwork   #JAMAai