ArticleScientific reports2025
Expert evaluation of ChatGPT accuracy and reliability for basic celiac disease frequently asked questions.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Performance of large language models as a source of clinical information on bacteriophage therapy.Npj viruses · 2026Article
- Quality of artificial intelligence-generated responses on pediatric celiac disease: Comparative assessment of Open AI ChatGPT and Google Gemini.JPGN reports · 2026Article
- Performance of large language models in answering frequently-asked questions on celiac disease.Journal of pediatric gastroenterology and nutrition · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence's (AI) role in providing information on Celiac Disease (CD) remains understudied. This study aimed to evaluate the accuracy and reliability of ChatGPT-3.5 in generating responses to 20 basic CD-related queries. This study assessed ChatGPT-3.5, the dominant publicly accessible version during the study period, to establish a benchmark for AI-assisted CD education. The accuracy of ChatGPT's responses to twenty frequently asked questions (FAQs) was assessed by two independent experts using a Likert scale, followed by categorization based on CD management domains. Inter-rater reliability (agreement between experts) was determined through cross-tabulation, Cohen's kappa, and Wilcoxon signed-rank tests. Intra-rater reliability (agreement within the same expert) was evaluated using the Friedman test with post hoc comparisons. ChatGPT demonstrated high accuracy in responding to CD FAQs, with expert ratings predominantly ranging from 4 to 5. While overall performance was strong, responses to management strategies excelled compared to those related to disease etiology. Inter-rater reliability analysis revealed moderate agreement between the two experts in evaluating ChatGPT's responses (κ = 0.22, p-value = 0.026). Although both experts consistently assigned high scores across different CD management categories, subtle discrepancies emerged in specific instances. Intra-rater reliability analysis indicated high consistency in scoring for one expert (F
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