Bridging the Translational Gap in Artificial Intelligence for Otology and Neurotology: Clinical Readiness, Limitations, and Research Priorities

Background and Objectives: Artificial intelligence (AI) has generated numerous proof-of-concept applications in otology and neurotology, but relatively few have progressed to routine clinical use. This narrative review evaluates not only what AI can do across audiovestibular medicine, but also how close individual applications are to clinical translation and which barriers continue to limit implementation. Materials and Methods: PubMed/MEDLINE, Scopus, and Web of Science were searched from database inception to 31 July 2026 using combinations of terms related to AI, machine learning, deep learning, hearing loss, cochlear implantation, tinnitus, vestibular disorders, nystagmus, temporal bone imaging, digital phenotyping, rehabilitation, and robotic surgery. Reference lists of relevant publications were also screened. Evidence was synthesized thematically and interpreted according to task maturity, external validation, clinical utility, workflow integration, and patient-safety requirements.