Deep Learning in Otolaryngology: Promises, Performance, and Pathways to Clinical Use
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.
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This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.