Using Large Language Models to Promote Health Equity

Emma Pierson, Ph.D., Divya Shanmugam, Ph.D., Rajiv Movva, B.S., Jon Kleinberg, Ph.D., Monica Agrawal, Ph.D., Mark Dredze, Ph.D., Kadija Ferryman, Ph.D., Judy Wawira Gichoya, M.D., M.S., Dan Jurafsky, Ph.D., Pang Wei Koh, Ph.D., Karen Levy, Ph.D., J.D., Sendhil Mullainathan, Ph.D., Ziad Obermeyer, M.D., M.Phil., Harini Suresh, Ph.D., and Keyon Vafa, Ph.D.
Abstract
While the discussion about the effects of large language models (LLMs) on health equity has been largely cautionary, LLMs also present significant opportunities for improving health equity. We highlight three such opportunities: improving the detection of human bias; creating structured datasets relevant to health equity; and improving equity of access to health information.

Using Large Language Models to Promote Health Equity