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Clinical decisions using AI must consider patient values

Nature Medicine volume 28, pages 229–232 (2022)Cite this article

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Built-in decision thresholds for AI diagnostics are ethically problematic, as patients may differ in their attitudes about the risk of false-positive and false-negative results, which will require that clinicians assess patient values.

Recent years have seen a surge of interest in medical applications of machine learning and artificial intelligence (AI) — and in the ethics of these applications1,2. In the near future, it is unlikely that AI technology will replace human decision-makers, but it is likely to assist human decision-makers in many contexts3. For example, machine learning can assist with the diagnosis of cancer from imaging data4, the mapping of the boundaries of tumors5, the treatment of sepsis6, the classification of field-based rapid HIV tests7 and the diagnosis of cognitive-motor dissociation in unresponsive patients with brain injuries8.

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Fig. 1: Six possible decision pathways using AI algorithms for breast cancer screening.

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Acknowledgements

We thank S. Bhalla, L. Kofi Bright, A. Houston, L. Hudetz, R. Short, J. Swamidass, K. Vredenburgh, Z. Ward, K. Wright and patient groups at Washington University in St Louis, Stanford University and Johns Hopkins University for their input and advice. A.K.J. acknowledges support from the National Institute of Biomedical Imaging and Bioengineering of the US National Institutes of Health (R01-EB031051 and R56-EB028287).

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Authors and Affiliations

  1. Centre for Philosophy of Natural and Social Science, London School of Economics and Political Science, London, UK

    Jonathan Birch

  2. McCoy Family Center for Ethics in Society and the Institute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA, USA

    Kathleen A. Creel

  3. Department of Biomedical Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA

    Abhinav K. Jha

  4. Department of Philosophy and Division of Biology and Biomedical Sciences, Washington University in St Louis, St Louis, MO, USA

    Anya Plutynski

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  1. Jonathan Birch
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  2. Kathleen A. Creel
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  3. Abhinav K. Jha
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  4. Anya Plutynski
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Contributions

All authors contributed to conceptualization, methodology (survey design), investigation (consulting patients), and writing (review and editing). J.B. wrote the original draft.

Corresponding author

Correspondence to Jonathan Birch.

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The authors declare no competing interests.

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Birch, J., Creel, K.A., Jha, A.K. et al. Clinical decisions using AI must consider patient values. Nat Med 28, 229–232 (2022). https://doi.org/10.1038/s41591-021-01624-y

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