Large language models and generative AI in telehealth: a responsible use lens.

ChatGPT artificial intelligence large language models responsible use telehealth

Journal

Journal of the American Medical Informatics Association : JAMIA
ISSN: 1527-974X
Titre abrégé: J Am Med Inform Assoc
Pays: England
ID NLM: 9430800

Informations de publication

Date de publication:
04 Mar 2024
Historique:
received: 18 12 2023
revised: 05 02 2024
accepted: 14 02 2024
medline: 5 3 2024
pubmed: 5 3 2024
entrez: 5 3 2024
Statut: aheadofprint

Résumé

This scoping review aims to assess the current research landscape of the application and use of large language models (LLMs) and generative Artificial Intelligence (AI), through tools such as ChatGPT in telehealth. Additionally, the review seeks to identify key areas for future research, with a particular focus on AI ethics considerations for responsible use and ensuring trustworthy AI. Following the scoping review methodological framework, a search strategy was conducted across 6 databases. To structure our review, we employed AI ethics guidelines and principles, constructing a concept matrix for investigating the responsible use of AI in telehealth. Using the concept matrix in our review enabled the identification of gaps in the literature and informed future research directions. Twenty studies were included in the review. Among the included studies, 5 were empirical, and 15 were reviews and perspectives focusing on different telehealth applications and healthcare contexts. Benefit and reliability concepts were frequently discussed in these studies. Privacy, security, and accountability were peripheral themes, with transparency, explainability, human agency, and contestability lacking conceptual or empirical exploration. The findings emphasized the potential of LLMs, especially ChatGPT, in telehealth. They provide insights into understanding the use of LLMs, enhancing telehealth services, and taking ethical considerations into account. By proposing three future research directions with a focus on responsible use, this review further contributes to the advancement of this emerging phenomenon of healthcare AI.

Identifiants

pubmed: 38441296
pii: 7618853
doi: 10.1093/jamia/ocae035
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : ARC Industrial Transformation Training Centre for Information Resilience
ID : IC200100022

Informations de copyright

© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Auteurs

Javad Pool (J)

ARC Industrial Transformation Training Centre for Information Resilience (CIRES), The University of Queensland, Brisbane 4072, Australia.
School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane 4072, Australia.

Marta Indulska (M)

ARC Industrial Transformation Training Centre for Information Resilience (CIRES), The University of Queensland, Brisbane 4072, Australia.
Business School, The University of Queensland, Brisbane 4072, Australia.

Shazia Sadiq (S)

ARC Industrial Transformation Training Centre for Information Resilience (CIRES), The University of Queensland, Brisbane 4072, Australia.
School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane 4072, Australia.

Classifications MeSH