As the use of artificial intelligence (AI) moves from being a curiosity to a necessity, it is clear that the benefit obtained from using AI models to prioritize care interventions is an interplay of the model’s performance, the capacity to intervene, and the benefit/harm profile of the intervention. We will begin the conversation reviewing the necessary data strategy to enable organization wide AI adoption and leading into a discussion of the core intuition behind foundation models. After a brief review of the kinds of use-cases that AI can serve across multiple medical specialties, we will discuss Stanford Healthcare’s efforts to shape the adoption of health AI tools to be useful, reliable, and fair so that they lead to cost-effective solutions that meet health care's needs.
Nigam H. Shah, MBBS, PhD
Professor of Medicine, and of Biomedical Data Science
Chief Data Scientist, Stanford Healthcare
Associate Dean for Research, School of Medicine
Associate Director, Stanford Center for Biomedical Informatics Research
At Stanford Healthcare, Dr. Nigam Shah leads artificial intelligence and data science efforts for advancing the scientific understanding of disease, improving the practice of clinical medicine and orchestrating the delivery of health care. He has authored over 200 scientific publications, is an inventor on eight patents and patent applications, and has co-founded three companies.