Bias and distrust in medicine have been perpetuated by the misuse of medical equations, algorithms and devices. Artificial intelligence (AI) can exacerbate these problems. However, AI also has potential to detect, mitigate and remedy the harmful effects of bias to build trust and improve healthcare for everyone.
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References
Buolamwini, J. Unmasking AI: My Mission to Protect What is Human in a World of Machines (Penguin Random House, 2023).
Devakumar, D. Executive summary. Lancet 400, 2095–2096 (2022).
Baptiste, D. L. et al. Henrietta Lacks and America’s dark history of research involving African Americans. Nurs. Open 9, 2236–2238 (2022).
Omiye, J. A., Lester, J. C., Spichak, S., Rotemberg, V. & Daneshjou, R. Large language models propagate race-based medicine. npj Digit. Med. 6, 195 (2023).
Bazargan, M. et al. Discrimination and medical mistrust in a racially and ethnically diverse sample of California adults. Ann. Fam. Med. 19, 4–15 (2021).
Valbuena, V. S. M. et al. Racial bias in pulse oximetry measurement among patients about to undergo extracorporeal membrane oxygenation in 2019–2020: a retrospective cohort study. Chest 161, 971–978 (2022).
Eneanya, N. D. et al. Health inequities and the inappropriate use of race in nephrology. Nat. Rev. Nephrol. 18, 84–94 (2022).
Obermeyer, Z. et al. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 447–453 (2019).
Alenichev, A., Kingori, P. & Grietens, K. P. Reflections before the storm: the AI reproduction of biased imagery in global health visuals. Lancet Glob. Health 11, e1496–e1498 (2023).
Folk, Z. Google stops Gemini AI from making images of people—after Musk calls service ‘woke’. Forbes go.nature.com/3Jh3p9X (2024).
Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. AI in health and medicine. Nat. Med. 28, 31–38 (2022).
Glynn, E., Ghersi, D. & Mona, S. Towards equitable MHC binding predictions: computational strategies to assess and reduce data bias. Preprint at bioRxiv https://doi.org/10.1101/2024.01.30.578103 (2024).
Birhane, A. et al. The forgotten margins of AI ethics. In Proc. 2022 ACM Conference on Fairness, Accountability, and Transparency 948–958 (ACM, 2022).
Moses, M. & Powers, K. In The Complex Alternative: Complexity Scientists on the COVID-19 Pandemic (eds Krakauer, D. & West, G.) T-031 (SFI Press, 2021).
Acknowledgements
The authors thank the University of New Mexico (UNM) Santa Fe Institute Interdisciplinary Working Group on Algorithmic Justice, Advance at UNM, and the UNM Office for the Vice President for Research for supporting this work.
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Moses, M.E., Gipson Rankin, S.M. Medical artificial intelligence should do no harm. Nat Rev Electr Eng (2024). https://doi.org/10.1038/s44287-024-00049-2
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DOI: https://doi.org/10.1038/s44287-024-00049-2
