Generative AI in Healthcare and Public Health

Abstract: Five years ago, I coauthored a short piece titled Machine learning in population health: Opportunities and threats. Now seems like an apt time to revisit it. We argued that AI could automate tasks that people do not like doing, cannot do fast enough, or cannot afford to do. The breakthroughs in generative AI over the last year have expanded the opportunities even further, but the threats have grown as well. We have very promising results from our preliminary investigations into the ability of large language models (LLMs) to identify underlying causes of death by analyzing so-called Verbal Autopsy Interviews, and I am optimistic about the potential for AI-assisted data analysis to broaden participation in technical analyses central to evidence-based public health. While I have only just begun to explore where generative AI fits into agent-based simulation modeling, it seems like a promising direction as well. However, the well-documented penchant of LLMs to hallucinate seems likely to amplify the challenges of explainability that have plagued nonparametric models in the past.

Bio: Abraham Flaxman, PhD, is an Associate Professor of Health Metrics Sciences at the Institute for Health Metrics and Evaluation (IHME) at the University of Washington. He is currently leading the development of a simulation platform to derive “what-if” results from Global Burden of Disease estimates and is engaged in methodological and operational research on verbal autopsy. Dr. Flaxman has previously designed software tools such as DisMod-MR that IHME uses to estimate the Global Burden of Disease, and the Bednet Stock-and-Flow Model, which has produced estimates of insecticide-treated net coverage in sub-Saharan Africa.