Treating health disparities with artificial intelligence

Healthcare is an imperfect practice, with disparities in care reflecting those in society. While algorithms may be misued to amplify biases, they may also be used to identify and correct disparities.

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  1. 1.

    Institute of Medicine (US). Committee on Understanding and Eliminating Racial and Ethnic Disparities in Health Care. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care (National Academies Press, 2003).

  2. 2.

    Wesson, D. E., Lucey, C. R. & Cooper, L. A. JAMA 322, 111–112 (2019).

  3. 3.

    Centers for Disease Control and Prevention. Pregnancy Mortality Surveillance System—Maternal and Infant Health (October 2019).

  4. 4.

    New York City Department of Health and Mental Hygiene. Severe Maternal Morbidity in New York City, 2008–2012 (New York City Department of Health and Mental Hygiene, 2016).

  5. 5.

    Singh, G. K. & Yu, S. M. Int. J. MCH AIDS 8, 19–31 (2019).

  6. 6.

    Martin, J. A. et al. Births: Final Data for 2017 (Centers for Disease Control and Prevention, 2018).

  7. 7.

    Matthews, T. J. et al. Infant Mortality Statistics From the 2013 Period Linked Birth/Infant Death Data Set (Centers for Disease Control and Prevention, 2015).

  8. 8.

    Zou, J. & Schiebinger, L. Nature 559, 324–326 (2018).

  9. 9.

    Adamson, A. S. & Smith, A. JAMA Dermatol. 154, 1247–1248 (2018).

  10. 10.

    Oh, S. S. et al. PLoS Med. 12, e1001918 (2015).

  11. 11.

    Haas, J. S. et al. Cancer 122, 611–617 (2016).

  12. 12.

    Herrera-Perez, D. et al. eLife 8, e45183 (2019).

  13. 13.

    Popejoy, A. B. & Fullerton, S. M. Nature 538, 161–164 (2016).

  14. 14.

    Gianfrancesco, M. A., Tamang, S., Yazdany, J. & Schmajuk, G. JAMA Intern. Med. 178, 1544–1547 (2018).

  15. 15.

    Alsan, M., Garrick, O. & Graziani, G. C. Does Diversity Matter for Health? Experimental Evidence from Oakland (National Bureau of Economic Research, 2018).

  16. 16.

    Greenwood, B. N., Carnahan, S. & Huang, L. Proc. Natl Acad. Sci. USA 115, 8569–8574 (2018).

  17. 17.

    Abebe, R., Hill, S. & Schwartz, H. A. Using search queries to understand health information needs in Africa. in Proceedings of the International AAAI Conference on Web and Social Media (Association for the Advancement of Artificial Intelligence, 2019).

  18. 18.

    Pfohl, S. et al. Creating fair models of atherosclerotic cardiovascular disease risk. in Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society 271–278 (Association for Computing Machinery, 2019).

  19. 19.

    Chen, I. Y., Szolovits, P. & Ghassemi, M. AMA J. Ethics 21, 167–179 (2019).

  20. 20.

    Buolamwini, J. & Gebru, T. in Proceedings of the 1st Conference on Fairness, Accountability and Transparency (eds Friedler, S. A. & Wilson, C.) 1–15 (Proceedings of Machine Learning Research, 2018).

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No funds were specifically solicited for this Comment. The principal investigators were partially supported by a CIFAR AI Chair at the Vector Institute and an NSERC Discovery Grant. M.G. is Canada Research Chair at the University of Toronto Departments of Computer Science and Medicine, and Canadian CIFAR AI Chair at the Vector Institute.

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Correspondence to Irene Y. Chen.

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Chen, I.Y., Joshi, S. & Ghassemi, M. Treating health disparities with artificial intelligence. Nat Med 26, 16–17 (2020) doi:10.1038/s41591-019-0649-2

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