Cargando…

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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Gilbert, Stephen, Anderson, Stuart, Daumer, Martin, Li, Phoebe, Melvin, Tom, Williams, Robin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10148205/
https://www.ncbi.nlm.nih.gov/pubmed/37058329
http://dx.doi.org/10.2196/43682
_version_ 1785034949697470464
author Gilbert, Stephen
Anderson, Stuart
Daumer, Martin
Li, Phoebe
Melvin, Tom
Williams, Robin
author_facet Gilbert, Stephen
Anderson, Stuart
Daumer, Martin
Li, Phoebe
Melvin, Tom
Williams, Robin
author_sort Gilbert, Stephen
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10148205
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher JMIR Publications
record_format MEDLINE/PubMed
spelling pubmed-101482052023-04-30 Learning From Experience and Finding the Right Balance in the Governance of Artificial Intelligence and Digital Health Technologies Gilbert, Stephen Anderson, Stuart Daumer, Martin Li, Phoebe Melvin, Tom Williams, Robin J Med Internet Res Viewpoint 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. JMIR Publications 2023-04-14 /pmc/articles/PMC10148205/ /pubmed/37058329 http://dx.doi.org/10.2196/43682 Text en ©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. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Viewpoint
Gilbert, Stephen
Anderson, Stuart
Daumer, Martin
Li, Phoebe
Melvin, Tom
Williams, Robin
Learning From Experience and Finding the Right Balance in the Governance of Artificial Intelligence and Digital Health Technologies
title Learning From Experience and Finding the Right Balance in the Governance of Artificial Intelligence and Digital Health Technologies
title_full Learning From Experience and Finding the Right Balance in the Governance of Artificial Intelligence and Digital Health Technologies
title_fullStr Learning From Experience and Finding the Right Balance in the Governance of Artificial Intelligence and Digital Health Technologies
title_full_unstemmed Learning From Experience and Finding the Right Balance in the Governance of Artificial Intelligence and Digital Health Technologies
title_short Learning From Experience and Finding the Right Balance in the Governance of Artificial Intelligence and Digital Health Technologies
title_sort learning from experience and finding the right balance in the governance of artificial intelligence and digital health technologies
topic Viewpoint
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10148205/
https://www.ncbi.nlm.nih.gov/pubmed/37058329
http://dx.doi.org/10.2196/43682
work_keys_str_mv AT gilbertstephen learningfromexperienceandfindingtherightbalanceinthegovernanceofartificialintelligenceanddigitalhealthtechnologies
AT andersonstuart learningfromexperienceandfindingtherightbalanceinthegovernanceofartificialintelligenceanddigitalhealthtechnologies
AT daumermartin learningfromexperienceandfindingtherightbalanceinthegovernanceofartificialintelligenceanddigitalhealthtechnologies
AT liphoebe learningfromexperienceandfindingtherightbalanceinthegovernanceofartificialintelligenceanddigitalhealthtechnologies
AT melvintom learningfromexperienceandfindingtherightbalanceinthegovernanceofartificialintelligenceanddigitalhealthtechnologies
AT williamsrobin learningfromexperienceandfindingtherightbalanceinthegovernanceofartificialintelligenceanddigitalhealthtechnologies