Author:
Vaira Luigi Angelo,Lechien Jerome R.,Abbate Vincenzo,Allevi Fabiana,Audino Giovanni,Beltramini Giada Anna,Bergonzani Michela,Boscolo-Rizzo Paolo,Califano Gianluigi,Cammaroto Giovanni,Chiesa-Estomba Carlos M.,Committeri Umberto,Crimi Salvatore,Curran Nicholas R.,di Bello Francesco,di Stadio Arianna,Frosolini Andrea,Gabriele Guido,Gengler Isabelle M.,Lonardi Fabio,Maniaci Antonino,Maglitto Fabio,Mayo-Yáñez Miguel,Petrocelli Marzia,Pucci Resi,Saibene Alberto Maria,Saponaro Gianmarco,Tel Alessandro,Trabalzini Franco,Trecca Eleonora M.C.,Vellone Valentino,Salzano Giovanni,De Riu Giacomo
Abstract
AbstractObjectiveTo propose and validate the Quality Assessment of Medical Artificial Intelligence (QAMAI), a tool specifically designed to assess the quality of health information provided by AI platforms.Study designobservational and valuative studySetting27 surgeons from 25 academic centers worldwide.MethodsThe QAMAI tool has been developed by a panel of experts following guidelines for the development of new questionnaires. A total of 30 responses from ChatGPT4, addressing patient queries, theoretical questions, and clinical head and neck surgery scenarios were assessed. Construct validity, internal consistency, inter-rater and test-retest reliability were assessed to validate the tool.ResultsThe validation was conducted on the basis of 792 assessments for the 30 responses given by ChatGPT4. The results of the exploratory factor analysis revealed a unidimensional structure of the QAMAI with a single factor comprising all the items that explained 51.1% of the variance with factor loadings ranging from 0.449 to 0.856. Overall internal consistency was high (Cronbach’s alpha=0.837). The Interclass Correlation Coefficient was 0.983 (95%CI 0.973-0.991; F(29,542)=68.3;p<0.001), indicating excellent reliability. Test-retest reliability analysis revealed a moderate-to-strong correlation with a Pearson’s coefficient of 0.876 (95%CI 0.859-0.891;p<0.001)ConclusionsThe QAMAI tool demonstrated significant reliability and validity in assessing the quality of health information provided by AI platforms. Such a tool might become particularly important/useful for physicians as patients increasingly seek medical information on AI platforms.
Publisher
Cold Spring Harbor Laboratory
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