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. 2024 Sep 1;31(9):2147-2150.
doi: 10.1093/jamia/ocae055.

Leveraging large language models to foster equity in healthcare

Jorge A Rodriguez  1   2 , Emily Alsentzer  1   2 , David W Bates  1   2
Affiliations

Leveraging large language models to foster equity in healthcare

Jorge A Rodriguez et al. J Am Med Inform Assoc. .

Abstract

Objectives: Large language models (LLMs) are poised to change care delivery, but their impact on health equity is unclear. While marginalized populations have been historically excluded from early technology developments, LLMs present an opportunity to change our approach to developing, evaluating, and implementing new technologies. In this perspective, we describe the role of LLMs in supporting health equity.

Materials and methods: We apply the National Institute on Minority Health and Health Disparities (NIMHD) research framework to explore the use of LLMs for health equity.

Results: We present opportunities for how LLMs can improve health equity across individual, family and organizational, community, and population health. We describe emerging concerns including biased data, limited technology diffusion, and privacy. Finally, we highlight recommendations focused on prompt engineering, retrieval augmentation, digital inclusion, transparency, and bias mitigation.

Conclusion: The potential of LLMs to support health equity depends on making health equity a focus from the start.

Keywords: artificial intelligence; digital inclusion; health disparities; health equity.

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Conflict of interest statement

D.W.B. reports grants and personal fees from EarlySense, personal fees from CDI Negev, equity from ValeraHealth, equity from Clew, equity from MDClone, personal fees and equity from AESOP, personal fees and equity from FeelBetter, personal fees and equity from Guided Clinical Solutions, and grants from IBM Watson Health, outside the submitted work. Additional authors do not have competing interests to declare.

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