Reliability of a generative artificial intelligence tool for pediatric familial Mediterranean fever: insights from a multicentre expert survey

• Published in: Pediatric rheumatology online journal Vol. 22; no. 1; pp. 78 - 11
• Main Authors: La Bella, Saverio, Attanasi, Marina, Porreca, Annamaria, Di Ludovico, Armando, Maggio, Maria Cristina, Gallizzi, Romina, La Torre, Francesco, Rigante, Donato, Soscia, Francesca, Ardenti Morini, Francesca, Insalaco, Antonella, Natale, Marco Francesco, Chiarelli, Francesco, Simonini, Gabriele, De Benedetti, Fabrizio, Gattorno, Marco, Breda, Luciana
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 23.08.2024; BMC
• Subjects: Artificial Intelligence; Child; Computer software industry; Disodium pamidronate; Familial Mediterranean fever; Familial Mediterranean Fever - diagnosis; FMF; Generative artificial intelligence; Humans; Medical care; Observer Variation; Pediatric rheumatology; Pediatrics; Quality management; Reproducibility of Results; Surveys; Surveys and Questionnaires; United States; AI; Pediatric rheumatology; Generative artificial intelligence; Artificial intelligence; FMF; Familial mediterranean fever
• DOI: 10.1186/s12969-024-01011-0
• ISSN: 1546-0096

Artificial 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). Fifteen 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. Median 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. AI 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.