Reply to: 'Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms' and 'Confounding factors need to be accounted for in assessing bias by machine learning algorithms'

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Laleh Seyyed-Kalantari et al. Nat Med. 2022 Jun.

. 2022 Jun;28(6):1161-1162.

doi: 10.1038/s41591-022-01854-8. Epub 2022 Jun 16.

No abstract available

Comment on

Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations.
Seyyed-Kalantari L, Zhang H, McDermott MBA, Chen IY, Ghassemi M. Seyyed-Kalantari L, et al. Nat Med. 2021 Dec;27(12):2176-2182. doi: 10.1038/s41591-021-01595-0. Epub 2021 Dec 10. Nat Med. 2021. PMID: 34893776 Free PMC article.
Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms.
Bernhardt M, Jones C, Glocker B. Bernhardt M, et al. Nat Med. 2022 Jun;28(6):1157-1158. doi: 10.1038/s41591-022-01846-8. Epub 2022 Jun 16. Nat Med. 2022. PMID: 35710993 No abstract available.
Confounding factors need to be accounted for in assessing bias by machine learning algorithms.
Mukherjee P, Shen TC, Liu J, Mathai T, Shafaat O, Summers RM. Mukherjee P, et al. Nat Med. 2022 Jun;28(6):1159-1160. doi: 10.1038/s41591-022-01847-7. Epub 2022 Jun 16. Nat Med. 2022. PMID: 35710994 No abstract available.

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1. Mukherjee, P. et al. Confounding factors need to be accounted for in assessing bias by machine learning algorithms (2022).
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