Applications and Future Perspectives of Large Language Models in Otolaryngology-Head and Neck Surgery: A Comprehensive Survey
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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 appli- cations 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 Re- ports. Furthermore, we categorized these studies by use case and systematically examined the models, datasets, and evalu- ation methods employed. Despite increasing adoption of LLMs in the ENT field, several challenges remain, including limit- ed model diversity, inconsistent evaluation standards, and ongoing issues with accuracy and fairness. We also contextual- ized 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 understand- ing, evaluating, and implementing LLMs or their advanced successors (e.g., large multimodal models, agents) in clinical and research settings.
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 appli- cations 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 Re- ports. Furthermore, we categorized these studies by use case and systematically examined the models, datasets, and evalu- ation methods employed. Despite increasing adoption of LLMs in the ENT field, several challenges remain, including limit- ed model diversity, inconsistent evaluation standards, and ongoing issues with accuracy and fairness. We also contextual- ized 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 understand- ing, evaluating, and implementing LLMs or their advanced successors (e.g., large multimodal models, agents) in clinical and research settings.