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Bridging Health Disparities in the Data-Driven World of Artificial Intelligence: A Narrative Review

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Journal of Racial and Ethnic Health Disparities Aims and scope Submit manuscript

Abstract

Background

Artificial intelligence (AI) holds exciting potential to revolutionize healthcare delivery in the United States. However, there are concerns about its potential to perpetuate disparities among historically marginalized populations.

Objective

Following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses, we conducted a narrative review of current literature on AI and health disparities in the United States. We aimed to answer the question, Does AI have the potential to reduce or eliminate health disparities, or will its use further exacerbate these disparities?

Methods

We searched the Ovid MEDLINE electronic database to identify and retrieve publications discussing AI and its impact on racial/ethnic health disparities. Articles were included if they discussed AI as a tool to mitigate racial health disparities with or without bias in developing and using AI.

Results

This review included 65 articles. We identified six themes of limitations in AI that impact health equity: (1) biases in AI can perpetuate and exacerbate racial and ethnic inequities; (2) equity in algorithms should be a priority; (3) lack of diversity in the field of AI is concerning; (4) the need for regulation and testing algorithms for accuracy; (5) ethical standards for AI in health care are needed; and (6) the importance of promoting transparency and accountability.

Conclusions

While AI promises to enhance healthcare outcomes and address equity concerns, risks and challenges are associated with its implementation. To maximize the use of AI, it must be approached with an equity lens during all phases of development.

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Data Availability

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Acknowledgements

Editorial assistance was provided by Moffitt Cancer Center’s Office of Scientific Publishing by Daley Drucker and Gerard Hebert; no compensation was given beyond their regular salaries.

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Authors and Affiliations

  1. Department of Health Outcomes and Behavior, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL, 33612, USA

    Anastasia Murphy, Kuan Bowen & B. Lee Green

  2. Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA

    Isaam M. El Naqa & Balaurunathan Yoga

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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Blinded Authors]. The first draft of the manuscript was written by [Blinded Authors] and all authors commented on previous versions of the manuscript. [Blinded Authors] critically revised the manuscript and all authors read and approved the final manuscript.

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Correspondence to Anastasia Murphy.

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Murphy, A., Bowen, K., Naqa, I.M.E. et al. Bridging Health Disparities in the Data-Driven World of Artificial Intelligence: A Narrative Review. J. Racial and Ethnic Health Disparities (2024). https://doi.org/10.1007/s40615-024-02057-2

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