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. Results: The most mature applications are narrow, well-defined tasks involving image or signal classification and anatomical segmentation, including audiogram pattern recognition, automated nystagmus extraction, temporal-bone segmentation, and vestibular schwannoma volumetry. By contrast, multimodal prognostic models, AI-guided treatment selection, digital phenotyping, autonomous decision support, and robotic or intraoperative systems remain less clinically established. Across domains, translation is constrained by small retrospective datasets, center- and device-specific acquisition patterns, inconsistent reference standards, limited external and prospective validation, inadequate reporting of calibration and failure modes, and scarce evidence of improved patient outcomes or workflow efficiency. Conclusions: The principal challenge for AI in otology and neurotology is no longer technical feasibility, but demonstration of generalizable clinical value. Future research should prioritize representative multicenter data, independent validation, prospective impact studies, clinically meaningful comparators, interoperability, explainability, fairness, and post-deployment surveillance. AI should be implemented as an auditable, human-supervised technology that strengthens specialist judgment and patient-centered care.