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

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

Journal

Journal of medical Internet research
ISSN: 1438-8871
Titre abrégé: J Med Internet Res
Pays: Canada
ID NLM: 100959882

Informations de publication

Date de publication:
14 04 2023
Historique:
received: 20 10 2022
accepted: 21 02 2023
revised: 17 01 2023
medline: 18 4 2023
entrez: 14 4 2023
pubmed: 15 4 2023
Statut: epublish

Résumé

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.

Identifiants

pubmed: 37058329
pii: v25i1e43682
doi: 10.2196/43682
pmc: PMC10148205
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e43682

Informations de copyright

©Stephen Gilbert, Stuart Anderson, Martin Daumer, Phoebe Li, Tom Melvin, Robin Williams. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.04.2023.

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Auteurs

Stephen Gilbert (S)

Else Kröner Fresenius Center for Digital Health, Technische Universität Dresden, Dresden, Germany.

Stuart Anderson (S)

School of Social and Political Science, University of Edinburgh, Edinburgh, United Kingdom.

Martin Daumer (M)

School of Computation, Information and Technology, Technische Universität München, Munich, Germany.

Phoebe Li (P)

School of Law, Politics and Sociology, University of Sussex, Brighton, United Kingdom.

Tom Melvin (T)

School of Medicine, Trinity College Dublin, Dublin, Ireland.

Robin Williams (R)

School of Social and Political Science, University of Edinburgh, Edinburgh, United Kingdom.

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Classifications MeSH