Health Equity in the Era of Large Language Models

Abstract

This commentary presents a summary of 8 major regulations and guidelines that have direct implications for the equitable design, implementation, and maintenance of health care-focused large language models (LLMs) deployed in the US. We grouped key equity issues for LLMs into 3 domains: (1) linguistic and cultural bias, (2) accessibility and trust, and (3) oversight and quality control. Solutions shared by these regulations and guidelines are to (1) ensure diverse representation in training data and in teams that develop artificial intelligence (AI) tools, (2) develop techniques to evaluate AI-enabled health care tool performance against real-world data, (3) ensure that AI used in health care is free of discrimination and integrates equity principles, (4) take meaningful steps to ensure access for patients with limited English proficiency, (5) apply AI tools to make workplaces more efficient and reduce administrative burdens, (6) require human oversight of AI tools used in health care delivery, and (7) ensure AI tools are safe, accessible, and beneficial while respecting privacy. There is an opportunity to prevent further embedding of existing disparities and issues in the health care system by enhancing health equity through thoughtfully designed and deployed LLMs.

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