Comparative Analysis of Information Quality in Pediatric Otorhinolaryngology: Clinicians, Residents, and Large Language Models

Abstract

Abstract Objective Pediatric otorhinolaryngology (ORL) addresses complex conditions in children, requiring a tailored approach for patients and families. With artificial intelligence (AI) gaining traction in medical applications, this study evaluates the quality of information provided by large language models (LLMs) in comparison to clinicians, identifying strengths and limitations in the field of pediatric ORL. Study Design Comparative blinded study. Setting Controlled research environment using LLMs. Methods Fifty‐four items of increasing difficulty, namely 18 theoretical questions, 18 clinical scenarios, and 18 patient questions, were posed to ChatGPT‐3.5, ‐4.0, ‐4o, Claude‐3, Gemini, Perplexity, Copilot, a second‐year resident, and an expert in the field of pediatric ORL. The Quality Analysis of Medical Artificial Intelligence (QAMAI) tool was used for blinded evaluation of the quality of medical information by a panel of expert members from the Young Otolaryngologists Group of the Italian Society of ORL and the International Federation of ORL Societies. Results LLMs performed comparably to specialist in theoretical and standardized clinical scenarios, with Bing Copilot achieving the highest QAMAI scores. However, AI responses lacked transparency in citing reliable sources and were less effective in addressing patient‐centered questions. Poor interrater agreement among reviewers highlighted challenges in distinguishing human‐generated from AI‐generated responses. Rhinology topics received the highest scores, whereas laryngology and patient‐centered questions showed lower agreement and performance. Conclusion LLMs show promise as supportive resources in pediatric ORL, particularly in theoretical learning and standardized cases. However, significant limitations remain, including source transparency and contextual communication in patient interactions. Human oversight is essential to mitigate risks. Future developments should focus on refining AI capabilities for evidence‐based and empathetic communication to support both clinicians and families.

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