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Accès ouvert déclaré 2025 book-chapter

“Can ChatGPT Answer Patient’s Questions?”: A Preliminary Analysis

1Citations signalées — pas une note de qualité
7Institutions déclarées
1Pays d’affiliation déclarés

Résumé fourni par la source

Whether ChatGPT's answers to medical questions are accurate, reliable, and trustworthy, and whether the public, not having a health background, knows how to evaluate ChatGPT's answers remains unclear. This study assessed ChatGPT's performance in answering medical questions posed by the public. An existing clinical question dataset of consumer questions from the NIH Genetic and Rare Diseases Information Center (GARD) was used for this study. API calls produced 1467 question-answer pairs for GPT-4-0613 (ChatGPT-4.0). 100 question-answer pairs were randomly selected as the sample of this study. They were evaluated on two criteria, Scientific Accuracy, and Comprehensiveness, with scales from 0 to 5. The results showed that ChatGPT-4.0 provided about 90% above average performance on scale points 4 and 5 on Scientific Accuracy, 84% on Comprehensiveness, and approximately 7% and 14% on average on scale point 3 on these two quality criteria. No statistical differences were found in the quality of answers to the questions following a question framework and those without. These study results indicate that healthcare consumers must consult healthcare providers and/or other reliable information resources to verify the answers and gauge the applicability to individual situations. Further studies can include investigating the impact of how the medical questions were asked on the quality of ChatGPT answers, comparing healthcare consumers' evaluations with healthcare and information professionals' evaluations, and so on.

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Contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
“Can ChatGPT Answer Patient’s Questions?”: A Preliminary Analysis
Date Crossref
07/08/2025
Éditeur
IOS Press
Type
book-chapter

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude et ne compte pas comme une seconde source scientifique indépendante.

Institutions déclarées

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Sujets associés

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

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