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Treating health disparities with artificial intelligence

Nature Medicine volume 26, pages 16–17 (2020)Cite this article

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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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References

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

    Article  Google Scholar 

  3. Centers for Disease Control and Prevention. Pregnancy Mortality Surveillance System—Maternal and Infant Health https://www.cdc.gov/reproductivehealth/maternalinfanthealth/pregnancy-mortality-surveillance-system.htm (October 2019).

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

    Article  PubMed  Google Scholar 

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

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  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).

Download references

Acknowledgements

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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Authors and Affiliations

  1. Massachusetts Institute of Technology, Cambridge, MA, USA

    Irene Y. Chen

  2. Vector Institute, Toronto, Ontario, Canada

    Shalmali Joshi & Marzyeh Ghassemi

  3. Departments of Computer Science and Medicine, University of Toronto, Toronto, Ontario, Canada

    Marzyeh Ghassemi

Authors
  1. Irene Y. Chen
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  2. Shalmali Joshi
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  3. Marzyeh Ghassemi
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Corresponding author

Correspondence to Irene Y. Chen.

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The authors declare no competing interests.

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

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