ArticleOtolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery2025
Comparative Analysis of Information Quality in Pediatric Otorhinolaryngology: Clinicians, Residents, and Large Language Models.
Article in Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery, 2025. 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.
- Three-dimensional printing and cochlear implantation: a systematic review.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Review
- Can Large Artificial Intelligence-Based Linguistic Models Help to Obtain Information About Burning Mouth Syndrome?Oral diseases · 2026Article
- Predicting recovery in Bell's palsy: the impact of early rehabilitation and prognostic indicators.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Comparative diagnostic accuracy of ChatGPT models in salivary gland disease: a multimodal vignette-based evaluation.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- AI at the Sella Turcica: Multi-Model Large Language Model Evaluation in Pituitary Adenomas.Brain & spine · 2026Article
- Applications and Future Perspectives of Large Language Models in Otolaryngology-Head and Neck Surgery: A Comprehensive Survey.Clinical and experimental otorhinolaryngology · 2025Review
- Telemedicine-Supported CPAP Therapy in Patients with Obstructive Sleep Apnea: Association with Treatment Adherence and Clinical Outcomes.Journal of clinical medicine · 2025Article
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Authors and funding
11 authors.
Funding
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Abstract
objectivePediatric 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
designComparative blinded study.
settingControlled research environment using LLMs.
methodsFifty-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.
resultsLLMs 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.
conclusionLLMs 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.