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A distributed approach to the regulation of clinical AI

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  • Trishan Panch
  • Erin Duralde
  • Heather Mattie
  • Gopal Kotecha
  • Leo Anthony Celi
  • Melanie Wright
  • Felix Greaves

Abstract

Regulation is necessary to ensure the safety, efficacy and equitable impact of clinical artificial intelligence (AI). The number of applications of clinical AI is increasing, which, amplified by the need for adaptations to account for the heterogeneity of local health systems and inevitable data drift, creates a fundamental challenge for regulators. Our opinion is that, at scale, the incumbent model of centralized regulation of clinical AI will not ensure the safety, efficacy, and equity of implemented systems. We propose a hybrid model of regulation, where centralized regulation would only be required for applications of clinical AI where the inference is entirely automated without clinician review, have a high potential to negatively impact the health of patients and for algorithms that are to be applied at national scale by design. This amalgam of centralized and decentralized regulation we refer to as a distributed approach to the regulation of clinical AI and highlight the benefits as well as the pre-requisites and challenges involved.

Suggested Citation

  • Trishan Panch & Erin Duralde & Heather Mattie & Gopal Kotecha & Leo Anthony Celi & Melanie Wright & Felix Greaves, 2022. "A distributed approach to the regulation of clinical AI," PLOS Digital Health, Public Library of Science, vol. 1(5), pages 1-8, May.
    • Handle: RePEc:plo:pdig00:0000040
    DOI: 10.1371/journal.pdig.0000040
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    Cited by:

    1. Fangyue Chen & Piyawat Kantagowit & Tanawin Nopsopon & Arisa Chuklin & Krit Pongpirul, 2023. "Prediction and diagnosis of chronic kidney disease development and progression using machine-learning: Protocol for a systematic review and meta-analysis of reporting standards and model performance," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-10, February.

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