Artificial intelligence in otolaryngology: current applications, limitations, and future perspectives

Abstract Purpose Artificial intelligence (AI) is increasingly integrated into modern otolaryngology practice and has emerged as one of the most rapidly evolving technologies in contemporary medicine. Recent advances in machine learning, deep learning, computer vision, and multimodal AI systems have accelerated the development of diagnostic and therapeutic applications across multiple otolaryngology subspecialties. Methods This narrative review was conducted through a structured literature search of PubMed/MEDLINE, Scopus, and Web of Science databases. Relevant publications evaluating artificial intelligence applications in otolaryngology were identified using combinations of predefined keywords. Priority was given to peer-reviewed studies published in English that investigated clinically relevant diagnostic, prognostic, surgical, educational, or workflow-related applications of artificial intelligence within otolaryngology. Both retrospective and prospective studies, review articles, landmark methodological studies, and representative publications with substantial influence on the field were considered. Results AI-assisted technologies have demonstrated promising clinical utility in diagnostic imaging, endoscopic assessment, audiology, rhinology, laryngology, vestibular medicine, surgical simulation, head and neck oncology, radiotherapy planning, and predictive analytics. Emerging evidence suggests that deep learning algorithms may enhance detection of sinonasal disease, facilitate automated image segmentation, predict lymph node metastasis, classify thyroid nodules, and support prognostic modeling in head and neck cancer patients. In addition, multimodal AI systems integrating radiologic, pathologic, molecular, and clinical data may further improve diagnostic precision and personalized treatment planning. Generative AI tools, including GPT-based large language models, have also demonstrated emerging applications in medical education, image interpretation, differential diagnosis, and clinical decision support. Conclusion Nevertheless, important methodological and translational challenges continue to limit broader clinical implementation. Current challenges include limited external validation, retrospective study design, dataset heterogeneity, algorithmic bias, lack of transparency, patient privacy concerns, medico-legal uncertainty, and the potential risks associated with automation bias. This narrative review summarizes current clinical applications of artificial intelligence in otolaryngology, discusses the strengths and limitations of currently available technologies, and highlights future perspectives for responsible clinical integration. Current evidence suggests that AI may serve as a valuable adjunctive tool in otolaryngology practice; however, further prospective multicenter studies, standardized validation frameworks, and ethical oversight remain necessary before widespread adoption can be achieved.