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When the algorithm passes the test but fails the patient

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  • Alex Mirugwe

Abstract

Tens of thousands of AI diagnostic tools have been authorised for clinical diagnosis worldwide, justified primarily by benchmark accuracy (performance on a fixed, curated test dataset), a number that says little about what the tool will do at the bedside. The field rests on three flawed foundations: datasets that do not reflect the populations served, development pipelines that exclude clinicians, and regulatory pathways that mistake on-paper accuracy for evidence of benefit. Closing the gap requires global coordination and the political will to treat clinical validation as a prerequisite rather than an afterthought.

Suggested Citation

  • Alex Mirugwe, 2026. "When the algorithm passes the test but fails the patient," PLOS Digital Health, Public Library of Science, vol. 5(10), pages 1-4, October.
    • Handle: RePEc:plo:pdig00:0001774
    DOI: 10.1371/journal.pdig.0001774
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