Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Reducing racial bias in AI models for clinical use requires a top-down intervention

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

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

    Article  Google Scholar 

  2. 2.

    Vyas, D. A., Eisenstein, L. G. & Jones, D. S. N. Engl. J. Med. 383, 874–882 (2020).

    Article  Google Scholar 

  3. 3.

    Owens, K. & Walker, A. Nat. Med. 26, 1327–1328 (2020).

    Article  Google Scholar 

  4. 4.

    Software as a Medical Device (SAMD): Clinical Evaluation — Guidance for Industry and Food and Drug Administration Staff (FDA, 2017).

  5. 5.

    Cruz Rivera, S. et al. Nat. Med. 26, 1351–1363 (2020).

    Article  Google Scholar 

  6. 6.

    Liu, X. et al. Nat. Med. 26, 1364–1374 (2020).

    Article  Google Scholar 

  7. 7.

    Geiger, H. J. in Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care (Smedley, B. D. et al.) 417–454 (National Academies Press, 2003).

  8. 8.

    Cone, D. C., Richardson, L. D., Todd, K. H., Betancourt, J. R. & Lowe, R. A. Acad. Emerg. Med. 10, 1176–1183 (2003).

    Article  Google Scholar 

  9. 9.

    Esteva, A. et al. Nature 542, 115–118 (2017).

    Article  Google Scholar 

  10. 10.

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

    Article  Google Scholar 

Download references

Acknowledgements

This work was made possible through funding from the Precision Medicine: Ethics, Politics and Culture Project, Precision Medicine & Society, Columbia University.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Supriya Kapur.

Ethics declarations

Competing interests

The author declares no competing interests.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kapur, S. Reducing racial bias in AI models for clinical use requires a top-down intervention. Nat Mach Intell 3, 460 (2021). https://doi.org/10.1038/s42256-021-00362-7

Download citation

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing