Quality and Accountability of ChatGPT in Health Care in Low- and Middle-Income Countries: Simulated Patient Study.
AI
AI integration
ChatGPT
LMIC
artificial intelligence
effectiveness
generative AI
health care
low- and middle-income countries
medication prescription
noncommunicable diseases
patient study
prescription
quality
quality and safety
reliability
simulated patient
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:
09 Sep 2024
09 Sep 2024
Historique:
received:
06
01
2024
accepted:
30
07
2024
revised:
21
04
2024
medline:
9
9
2024
pubmed:
9
9
2024
entrez:
9
9
2024
Statut:
epublish
Résumé
Using simulated patients to mimic 9 established noncommunicable and infectious diseases, we assessed ChatGPT's performance in treatment recommendations for common diseases in low- and middle-income countries. ChatGPT had a high level of accuracy in both correct diagnoses (20/27, 74%) and medication prescriptions (22/27, 82%) but a concerning level of unnecessary or harmful medications (23/27, 85%) even with correct diagnoses. ChatGPT performed better in managing noncommunicable diseases than infectious ones. These results highlight the need for cautious AI integration in health care systems to ensure quality and safety.
Identifiants
pubmed: 39250188
pii: v26i1e56121
doi: 10.2196/56121
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e56121Informations de copyright
©Yafei Si, Yuyi Yang, Xi Wang, Jiaqi Zu, Xi Chen, Xiaojing Fan, Ruopeng An, Sen Gong. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 09.09.2024.