There have been tremendous advances in generative AI such as ChatGPT and DALLE. This talk will explore how generative models can power biomedical discoveries by expanding the design space of medicine while balancing complex tradeoffs. I will illustrate this through three examples. We will first discuss how to use generative AI to design and experimentally validate novel drugs. Then we will apply a similar generative approach to guide the design of clinical trials to make trials more efficient and inclusive. Finally, we will demonstrate how to build (using Twitter!) visual-language models to index complex biomedical data. Throughout, I will highlight some of the key open challenges with generative AI related to bias amplification and behavioral drift.  

James Zou is an assistant professor of Biomedical Data Science, CS and EE at Stanford University. He is also the faculty director of Stanford AI4Health. He works on both improving the foundations of ML–-by making models more trustworthy and reliable–-as well as in-depth scientific and clinical applications. Many of his innovations are widely used in tech and biotech industries.  He has received a Sloan Fellowship, an NSF CAREER Award, two Chan-Zuckerberg Investigator Awards, a Top Ten Clinical Achievement Award, several best paper awards, and faculty awards from Google, Amazon, Tencent and Adobe.