Evaluating ChatGPT's moral competence in health care-related ethical problems.

artificial intelligence clinical decision-making ethics judgement morals

Journal

JAMIA open
ISSN: 2574-2531
Titre abrégé: JAMIA Open
Pays: United States
ID NLM: 101730643

Informations de publication

Date de publication:
Oct 2024
Historique:
received: 27 02 2024
revised: 17 06 2024
accepted: 24 06 2024
medline: 10 7 2024
pubmed: 10 7 2024
entrez: 10 7 2024
Statut: epublish

Résumé

Artificial intelligence tools such as Chat Generative Pre-trained Transformer (ChatGPT) have been used for many health care-related applications; however, there is a lack of research on their capabilities for evaluating morally and/or ethically complex medical decisions. The objective of this study was to assess the moral competence of ChatGPT. This cross-sectional study was performed between May 2023 and July 2023 using scenarios from the Moral Competence Test (MCT). Numerical responses were collected from ChatGPT 3.5 and 4.0 to assess individual and overall stage scores, including C-index and overall moral stage preference. Descriptive analysis and 2-sided Student's A total of 100 iterations of the MCT were performed and moral preference was found to be higher in the latter Kohlberg-derived arguments. ChatGPT 4.0 was found to have a higher overall moral stage preference (2.325 versus 1.755) when compared to ChatGPT 3.5. ChatGPT 4.0 was also found to have a statistically higher C-index score in comparison to ChatGPT 3.5 (29.03 ± 11.10 versus 19.32 ± 10.95, ChatGPT 3.5 and 4.0 trended towards higher moral preference for the latter stages of Kohlberg's theory for both dilemmas with C-indices suggesting medium moral competence. However, both models showed moderate variation in C-index scores indicating inconsistency and further training is recommended. ChatGPT demonstrates medium moral competence and can evaluate arguments based on Kohlberg's theory of moral development. These findings suggest that future revisions of ChatGPT and other large language models could assist physicians in the decision-making process when encountering complex ethical scenarios.

Identifiants

pubmed: 38983845
doi: 10.1093/jamiaopen/ooae065
pii: ooae065
pmc: PMC11233145
doi:

Types de publication

Journal Article

Langues

eng

Pagination

ooae065

Informations de copyright

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

Déclaration de conflit d'intérêts

The authors declare no conflicts of interest.

Auteurs

Ahmed A Rashid (AA)

Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL 32608, United States.

Ryan A Skelly (RA)

Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL 32608, United States.

Carlos A Valdes (CA)

Department of Surgery, University of Florida College of Medicine, Gainesville, FL 32608, United States.

Pruthvi P Patel (PP)

Department of Research, Alabama College of Osteopathic Medicine, Dothan, AL 36303, United States.

Lauren B Solberg (LB)

Department of Community Health and Family Medicine, University of Florida College of Medicine, Gainesville, FL 32608, United States.

Christopher R Giordano (CR)

Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL 32608, United States.

François Modave (F)

Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL 32608, United States.

Classifications MeSH