Thu, 21-Oct-2021 / 4:00pm / Packard 101
Clinical trials are both the gate-keeper and bottleneck of medicine. They can be very costly and challenging to conduct. This talk explores how AI can make trials more efficient and, on the flip side, how to use trials to evaluate AI rigorously. I will first discuss Trial Pathfinder, a computational framework that generates synthetic patient cohorts from medical records to optimize cancer trial designs (Liu et al. Nature 2021). Trial Pathfinder enables clinical trials to be more inclusive, benefiting diverse patients and trial sponsors. In the 2nd part, I will discuss insights that we learned from conducting some of the first trials testing real-time AIs at Stanford and analyzing data from >100 FDA-approved medical AIs (Wu et al. Nature Medicine 2021). These analyses raise new technical questions and approaches to audit ML models and understand why it makes certain mistakes, which are critical to making ML more trustworthy.
James Zou is an assistant professor of biomedical data science and, by courtesy, of CS and EE at Stanford University. He works on making AI more trustworthy, reliable and fair. He is particularly interested in developing AI to improve health outcomes, enable biotech discoveries, and make medical care more accessible. His group develops theoretical foundations and new algorithms and also deploys these new methods in hospitals and the biotech industry. James has received a Sloan Fellowship, NSF CAREER Award, Chan-Zuckerberg Investigator award, faculty awards from Google, Tencent and Amazon, and multiple best paper awards.