Using Artificial Intelligence to Optimize Diagnosis and Management of Vestibular Disorders: A Narrative Review
Elizabeth Tobener, Charles EllisPurpose:
This narrative review examines the role of artificial intelligence (AI) in enhancing the diagnosis and management of vestibular disorders, including benign paroxysmal positional vertigo, vestibular migraine, and Ménière's disease. These conditions are often misdiagnosed due to overlapping symptoms and limited access to specialized testing.
Method:
A literature search was conducted using PubMed, Scopus, IEEE Xplore, and Google Scholar for articles published between 2020 and 2025. Eligible studies focused on AI applications in vestibular diagnosis, monitoring, or therapy. Findings were organized thematically based on application domains and clinical relevance.
Results:
AI algorithms demonstrated improved diagnostic accuracy through analysis of clinical data, symptom patterns, and wearable sensor input. AI-enabled tools enhanced remote assessment and symptom tracking, while machine learning-guided rehabilitation improved adherence and outcomes. Despite these advances, challenges such as data quality, model transparency, and clinical adoption persist.
Conclusions:
AI holds significant promise for transforming vestibular care through improved diagnostics and personalized management. Future work should prioritize multimodal data integration, longitudinal validation, and explainable AI to support responsible and widespread clinical implementation.