Eleni V Litsou

This narrative review, based on a structured search of PubMed/MEDLINE, Scopus, and Web of Science, maps the clinical applications of artificial intelligence across otolaryngology subspecialties, noting that deep learning shows potential in sinonasal disease detection, automated image segmentation, lymph node metastasis prediction, thyroid nodule classification, and prognostic modeling in head and neck cancer, while multimodal systems and generative large language models are emerging in medical education, image interpretation, differential diagnosis, and clinical decision support; however, limited external validation, retrospective designs, dataset heterogeneity, algorithmic bias, lack of transparency, privacy concerns, medico-legal uncertainty, and automation bias still constrain broad imp

This narrative review, based on a structured search of PubMed/MEDLINE, Scopus, and Web of Science, maps the clinical applications of artificial intelligence across otolaryngology subspecialties, noting that deep learning shows potential in sinonasal disease detection, automated image segmentation, lymph node metastasis prediction, thyroid nodule classification, and prognostic modeling in head and neck cancer, while multimodal systems and generative large language models are emerging in medical education, image interpretation, differential diagnosis, and clinical decision support; however, limited external validation, retrospective designs, dataset heterogeneity, algorithmic bias, lack of transparency, privacy concerns, medico-legal uncertainty, and automation bias still constrain broad imp