RESUMO
Background: To investigate the perception of young European otolaryngologists (OTOs), i.e., head and neck surgeons, toward transoral robotic surgery (TORS). Methods: Members of the Young Confederation of European Otorhinolaryngology-Head and Neck Surgery and Young Otolaryngologists of International Federation of Otorhinolaryngological Societies were surveyed about TORS perception and practice. Results: The survey was completed by 120 young OTOS (26%). The most important barriers to TORS were robot availability (73%), cost (69%), and lack of training (37%). The participants believed that the main benefits include better surgical filed view (64%), shorter hospital stay (62%), and better postoperative outcomes (61%) than the conventional approach. Head and neck surgeons considered cT1-T2 oropharyngeal cancers (94%), resection of base of tongue for sleep apnea (86%), or primary unknown cancer (76%) as the most appropriate indications. A total of 67% of TORS surgeons assessed themselves as adequately trained in TORS. Conclusions: Young European OTOs report positive perception, adoption, and knowledge of TORS. The cost-related unavailability and the lack of training or access are reported to be the most important barriers for the spread of TORS.
RESUMO
BACKGROUND: The widespread diffusion of Artificial Intelligence (AI) platforms is revolutionizing how health-related information is disseminated, thereby highlighting the need for tools to evaluate the quality of such information. This study aimed to 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. METHODS: The 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 by 27 reviewers from 25 academic centers worldwide. Construct validity, internal consistency, inter-rater and test-retest reliability were assessed to validate the tool. RESULTS: The 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). CONCLUSIONS: The 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.
