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Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms

Nature Medicine volume 28, pages 1157–1158 (2022)Cite this article

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Matters Arising to this article was published on 16 June 2022

The Original Article was published on 10 December 2021

arising from L. Seyyed-Kalantari et al. Nat. Med. https://doi.org/10.1038/s41591-021-01595-0 (2021).

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Acknowledgements

We thank X. Liu, A. Denniston and M. McCradden for helpful discussions. M.B. is funded through an Imperial College London President’s PhD Scholarship. C.J. is supported by Microsoft Research and EPSRC through the Microsoft PhD Scholarship Programme. B.G. is supported through funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 757173, Project MIRA, ERC-2017-STG).

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  1. The authors contributed equally: Mélanie Bernhardt, Charles Jones, Ben Glocker.

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  1. Department of Computing, Imperial College London, London, United Kingdom

    Mélanie Bernhardt, Charles Jones & Ben Glocker

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  1. Mélanie Bernhardt
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  2. Charles Jones
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  3. Ben Glocker
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Contributions

The authors contributed equally to this work in terms of formulating the arguments, interpreting the available evidence and cowriting the manuscript.

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Correspondence to Ben Glocker.

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Competing interests

B.G. is a part-time employee of HeartFlow and Kheiron Medical Technologies and holds stock options with both as part of the standard compensation package. M.B. and C.J. declare no competing interests.

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Bernhardt, M., Jones, C. & Glocker, B. Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms. Nat Med 28, 1157–1158 (2022). https://doi.org/10.1038/s41591-022-01846-8

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