Highlight
– A comprehensive narrative review (2020–2025) screened 1,422 articles and synthesized 327 original deep learning (DL) studies in otolaryngology, grouped into detection/diagnosis (55%), segmentation (28%), prediction/prognostics (5%), and emerging applications (12%).
– Proof-of-concept DL models frequently achieved expert-comparable diagnostic accuracy (examples: nasopharyngeal carcinoma detection 92%, laryngeal malignancy 86%, otologic pathology >95%), but prognostic work and prospective, multi-institutional validation remain sparse.
