ArticleEuropean 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 Surgery2025
Chasing sleep physicians: ChatGPT-4o on the interpretation of polysomnographic results.
Article in 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, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Performance of nine large language models on real-world polysomnography interpretation for obstructive sleep apnea: a multi-dimensional comparative analysis.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
- Human-Centered AI in Sleep Health Management: Scoping Review of Stakeholder Perspectives and Co-Design Practices.Journal of medical Internet research · 2026Article
- Guideline-based benchmarking of large language models in otorhinolaryngology using 250 real-world cases.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
- Patients' perception towards large language models in otorhinolaryngology, head and neck surgery: a single-centre survey.Frontiers in digital health · 2026Article
- Performance of large language models on sleep medicine certification examination: a comprehensive multi-model analysis.Frontiers in medicine · 2026Article
- Evaluating Locally Run Large Language Models (Gemma 2, Mistral Nemo, and Llama 3) for Outpatient Otorhinolaryngology Care: Retrospective Study.JMIR formative research · 2025Article
- Are ChatGPT's Answers to Questions About Salivary Gland Diseases Accurate and Reliable?Turkish archives of otorhinolaryngology · 2025Article
- Comparative Analysis of Information Quality in Pediatric Otorhinolaryngology: Clinicians, Residents, and Large Language Models.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2025Article
- Evaluating Locally Run Large Language Models for Obstructive Sleep Apnea Diagnosis and Treatment: A Real-World Polysomnography Study.Nature and science of sleep · 2025Article
- Simulation-Based Evaluation of Large Language Models for Comorbidity Detection in Sleep Medicine - a Pilot Study on ChatGPT o1 Preview.Nature and science of sleep · 2025Article
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12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundFrom a healthcare professional's perspective, the use of ChatGPT (Open AI), a large language model (LLM), offers huge potential as a practical and economic digital assistant. However, ChatGPT has not yet been evaluated for the interpretation of polysomnographic results in patients with suspected obstructive sleep apnea (OSA). AIMS/
objectivesTo evaluate the agreement of polysomnographic result interpretation between ChatGPT-4o and a board-certified sleep physician and to shed light into the role of ChatGPT-4o in the field of medical decision-making in sleep medicine. MATERIAL AND
methodsFor this proof-of-concept study, 40 comprehensive patient profiles were designed, which represent a broad and typical spectrum of cases, ensuring a balanced distribution of demographics and clinical characteristics. After various prompts were tested, one prompt was used for initial diagnosis of OSA and a further for patients with positive airway pressure (PAP) therapy intolerance. Each polysomnographic result was independently evaluated by ChatGPT-4o and a board-certified sleep physician. Diagnosis and therapy suggestions were analyzed for agreement.
resultsChatGPT-4o and the sleep physician showed 97% (29/30) concordance in the diagnosis of the simple cases. For the same cases the two assessment instances unveiled 100% (30/30) concordance regarding therapy suggestions. For cases with intolerance of treatment with positive airway pressure (PAP) ChatGPT-4o and the sleep physician revealed 70% (7/10) concordance in the diagnosis and 44% (22/50) concordance for therapy suggestions. CONCLUSION AND SIGNIFICANCE: Precise prompting improves the output of ChatGPT-4o and provides sleep physician-like polysomnographic result interpretation. Although ChatGPT shows some shortcomings in offering treatment advice, our results provide evidence for AI assisted automation and economization of polysomnographic interpretation by LLMs. Further research should explore data protection issues and demonstrate reproducibility with real patient data on a larger scale.
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