Learning From Experience and Finding the Right Balance in the Governance of Artificial Intelligence and Digital Health Technologies

Stephen Gilbert, Stuart Anderson, Martin Daumer, Phoebe Li, Tom Melvin, Robin Williams

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Artificial intelligence (AI) and machine learning medical tools have the potential to be transformative in care delivery; however, this change will only be realized if accompanied by effective governance that ensures patient safety and public trust. Recent digital health initiatives have called for tighter governance of digital health. A correct balance must be found between ensuring product safety and performance while also enabling the innovation needed to deliver better approaches for patients and affordable efficient health care for society. This requires innovative, fit-for-purpose approaches to regulation. Digital health technologies, particularly AI-based tools, pose specific challenges to the development and implementation of functional regulation. The approaches of regulatory science and “better regulation” have a critical role in developing and evaluating solutions to these problems and ensuring effective implementation. We describe the divergent approaches of the European Union and the United States in the implementation of new regulatory approaches in digital health, and we consider the United Kingdom as a third example, which is in a unique position of developing a new post-Brexit regulatory framework.
Original languageEnglish
Article numbere43682
Pages (from-to)1-11
JournalJournal of medical Internet research
Volume25
Issue number1
DOIs
Publication statusPublished - 14 Apr 2023

Keywords / Materials (for Non-textual outputs)

  • artificial intelligence
  • machine learning
  • regulation
  • algorithm change protocol
  • health care
  • regulatory framework
  • medical tool
  • tool
  • patient
  • intervention
  • safety
  • performance
  • technology
  • implementation

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