ArticlePediatric rheumatology online journal2024
Reliability of a generative artificial intelligence tool for pediatric familial Mediterranean fever: insights from a multicentre expert survey.
Article in Pediatric rheumatology online journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Utility of Large Language Models for Congenital Microtia Reconstruction Education: Comparison of the Performance of Claude, GPT, and Gemini.Aesthetic plastic surgery · 2026Article
- Artificial intelligence in rheumatology and paediatric rheumatology: insights from an international survey by EMEUNET.EULAR rheumatology open · 2026Article
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- Evaluating Large Language Models for Preoperative Patient Education in Superior Capsular Reconstruction: Comparative Study of Claude, GPT, and Gemini.JMIR perioperative medicine · 2025Article
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- Familial Mediterranean fever in children from central-southern Italy: a multicentric retrospective cohort study.Clinical rheumatology · 2024Article
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) has become a popular tool for clinical and research use in the medical field. The aim of this study was to evaluate the accuracy and reliability of a generative AI tool on pediatric familial Mediterranean fever (FMF).
methodsFifteen questions repeated thrice on pediatric FMF were prompted to the popular generative AI tool Microsoft Copilot with Chat-GPT 4.0. Nine pediatric rheumatology experts rated response accuracy with a blinded mechanism using a Likert-like scale with values from 1 to 5.
resultsMedian values for overall responses at the initial assessment ranged from 2.00 to 5.00. During the second assessment, median values spanned from 2.00 to 4.00, while for the third assessment, they ranged from 3.00 to 4.00. Intra-rater variability showed poor to moderate agreement (intraclass correlation coefficient range: -0.151 to 0.534). A diminishing level of agreement among experts over time was documented, as highlighted by Krippendorff's alpha coefficient values, ranging from 0.136 (at the first response) to 0.132 (at the second response) to 0.089 (at the third response). Lastly, experts displayed varying levels of trust in AI pre- and post-survey.
conclusionsAI has promising implications in pediatric rheumatology, including early diagnosis and management optimization, but challenges persist due to uncertain information reliability and the lack of expert validation. Our survey revealed considerable inaccuracies and incompleteness in AI-generated responses regarding FMF, with poor intra- and extra-rater reliability. Human validation remains crucial in managing AI-generated medical information.
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