ReviewClinical and experimental otorhinolaryngology2025
Applications and Future Perspectives of Large Language Models in Otolaryngology-Head and Neck Surgery: A Comprehensive Survey.
Review in Clinical and experimental otorhinolaryngology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- "MELMA" in otolaryngology: Medical evaluation of large language model answers. Clinician-rated scoring (MELMA-Q) and web-based auditing (MELMA-W) novel tools for AI assessment.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
- Artificial Intelligence in Head and Neck Surgical Oncology: A State-of-the-Art Review.Journal of clinical medicine · 2026Review
- Evaluating the efficacy and readability of advanced large language models in responding to patients' frequently asked questions about chronic rhinosinusitis: a 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
- 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
- Sensory changes in the chin and lower lip after transoral endoscopic thyroidectomy.Surgical endoscopy · 2026Observational
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Since the release of ChatGPT, large language models (LLMs) have rapidly expanded into professional domains, including medicine. These models, trained on extensive text corpora, including the medical literature, have demonstrated remarkable capabilities in tasks such as clinical decision support, research assistance, and education. This review focuses on LLM applications in otolaryngology-head and neck surgery (Ear, Nose, and Throat [ENT]). We analyzed 25 studies published between January 2022 and March 2025 in ENT journals ranked in the top 25% (Q1) according to the 2023 Journal Citation Reports. Furthermore, we categorized these studies by use case and systematically examined the models, datasets, and evaluation methods employed. Despite increasing adoption of LLMs in the ENT field, several challenges remain, including limited model diversity, inconsistent evaluation standards, and ongoing issues with accuracy and fairness. We also contextualized LLM research trends within the broader medical domain. Five key areas were identified for advancing clinical-grade LLMs: robust evaluation frameworks, external source-based generation, multimodal integration, agent-based reasoning, and model explainability. Our findings provide ENT clinicians and researchers with a practical foundation for understanding, evaluating, and implementing LLMs or their advanced successors (e.g., large multimodal models, agents) in clinical and research settings.
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Registered trials
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.