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Confounding factors need to be accounted for in assessing bias by machine learning algorithms

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Matters Arising to this article was published on 16 June 2022

The Original Article was published on 10 December 2021

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References

  1. Seyyed-Kalantari, L. et al. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat. Med. 27, 2176–2182 (2021).

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  2. Seyyed-Kalantari, L., Liu, G., McDermott, M., Chen, I. Y. & Ghassemi, M. CheXclusion: fairness gaps in deep chest X-ray classifiers. In Pacific Symposium on Biocomputing 2021 (eds Altman, R. B. et al.) 232–243 (World Scientific Publishing, 2021).

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Authors and Affiliations

  1. Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA

    Pritam Mukherjee, Thomas C. Shen, Jianfei Liu, Tejas Mathai, Omid Shafaat & Ronald M. Summers

Authors
  1. Pritam Mukherjee
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  2. Thomas C. Shen
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  3. Jianfei Liu
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  4. Tejas Mathai
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  5. Omid Shafaat
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  6. Ronald M. Summers
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Contributions

P.M. contributed to conception, manuscript drafting and manuscript editing. T.C.S. contributed to conception, manuscript drafting and manuscript editing. J.L., T.M. and O.S. performed manuscript drafting and manuscript editing. R.M.S. contributed to conception, manuscript drafting, manuscript editing and supervision.

Corresponding author

Correspondence to Pritam Mukherjee.

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Competing interests

R.M.S. receives royalties for patents or software licenses from iCAD, Philips, PingAn, ScanMed and Translation Holdings. The laboratory of R.M.S. receives research support through a Cooperative Research and Development Agreement from PingAn. All other authors declare no competing interests.

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Mukherjee, P., Shen, T.C., Liu, J. et al. Confounding factors need to be accounted for in assessing bias by machine learning algorithms. Nat Med 28, 1159–1160 (2022). https://doi.org/10.1038/s41591-022-01847-7

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  • DOI: https://doi.org/10.1038/s41591-022-01847-7

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