References
Pan, I. et al. Am. J. Roentgenol. 213, 568–574 (2019).
Larrazabal, A. J. et al. Proc. Natl Acad. Sci. USA 117, 12592–12594 (2020).
Seyyed-Kalantari, L. et al. Nat. Med. 27, 2176–2182 (2021).
Howard, F. M. et al. Nat. Commun. 12, 4423 (2021).
Daneshjou, R. et al. Sci. Adv. 8, eabq6147 (2022).
Beheshtian, E. et al. Radiology 306, 220505 (2022).
Tolkachev, A. et al. IEEE J. Biomed. Health Inform. 25, 1660–1672 (2021).
Chen, J. et al. In Proc. Conference on Fairness, Accountability, and Transparency 339–348 (Association for Computing Machinery, 2019).
Bharti, B. et al. Preprint at arXiv, https://doi.org/10.48550/arXiv.2207.12497 (2022).
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Garin, S.P., Parekh, V.S., Sulam, J. et al. Medical imaging data science competitions should report dataset demographics and evaluate for bias. Nat Med 29, 1038–1039 (2023). https://doi.org/10.1038/s41591-023-02264-0
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DOI: https://doi.org/10.1038/s41591-023-02264-0
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