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...
Autores principales: | , , , , , |
---|---|
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 |