Beyond the hype: large language models propagate race-based medicine

Abstract

Importance Large language models (LLMs) are being integrated into healthcare systems; but these models recapitulate harmful, race-based medicine. Objective The objective of this study is to assess whether four commercially available large language models (LLMs) propagate harmful, inaccurate, race-based content when responding to eight different scenarios that historically included race-based medicine or widespread misconceptions around race. Evidence Review Questions were derived from discussion among 4 physician experts and prior work on race-based medical misconceptions of medical trainees. Findings We assessed four large language models with eight different questions that were interrogated five times each with a total of forty responses per a model. All models had examples of perpetuating race-based medicine in their responses. Models were not always consistent in their responses when asked the same question repeatedly. Conclusions and Relevance LLMs are being proposed for use in the healthcare setting, with some models already connecting to electronic health record systems. However, this study shows that based on our findings, these LLMs could potentially cause harm by perpetuating debunked, racist concepts.

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