Clinical bottom line
AI-driven personalized rehabilitation for chronic vestibular disorders represents a promising, hypothesis-generating approach to optimize vestibular therapy by tailoring interventions to individual patient profiles. Emerging evidence suggests that integrating AI technologies—such as digital human twins and decision support systems—may enhance the precision and adaptability of vestibular rehabilitation programs, potentially improving functional outcomes and patient adherence. However, current data remain preliminary, and rigorous clinical validation is required before widespread clinical adoption.
What the evidence shows
Chronic vestibular disorders, including peripheral vestibular dysfunction, often result in persistent dizziness, imbalance, and reduced quality of life, necessitating individualized rehabilitation strategies (Ernst et al., 2024; Clinical Otology, 2015). Traditional vestibular rehabilitation relies on standardized exercise protocols, which may not fully address patient-specific deficits or adapt dynamically to progress.
Recent advances in AI-driven physiotherapy highlight the potential for personalized rehabilitation plans that integrate patient-specific biomechanical data, symptom profiles, and response patterns (Mohite et al., 2026). AI decision support systems can analyze complex datasets to recommend tailored exercise regimens, optimize intensity, and adjust therapy in real time, potentially enhancing efficacy and adherence (Mohite et al., 2026).
Digital human twins—virtual biomechanical models of patients—offer a novel platform for simulating vestibular function and predicting rehabilitation outcomes, enabling clinicians to customize interventions with unprecedented precision (Gökboyun et al., 2026). These models incorporate individual anatomical and functional parameters, allowing iterative refinement of therapy plans.
While AI-driven personalized rehabilitation has been explored in related fields such as physiotherapy and oncology, its application to vestibular disorders is emerging. The foundational principles of integrating neurophysiological data, patient preferences, and real-time monitoring align with broader trends in AI-enabled personalized medicine (Chen Jiaju et al., 2025; Mohite et al., 2026).
Caveats and uncertainty
Despite promising conceptual frameworks, direct clinical evidence supporting AI-driven personalized vestibular rehabilitation remains limited. Most studies to date are preliminary, focusing on system development, feasibility, or extrapolation from related rehabilitation domains rather than large-scale clinical trials in vestibular populations.
The heterogeneity of vestibular disorders and variability in symptomatology pose challenges for AI model generalizability and validation. Ethical considerations around data privacy, patient consent, and algorithm transparency must be addressed, particularly given the sensitive nature of neurological rehabilitation data (Praveenbalaji et al., 2026).
Moreover, integration of AI tools into routine clinical workflows requires clinician training, infrastructure investment, and patient engagement strategies, which may limit immediate applicability. The cost-effectiveness and long-term impact on functional recovery and quality of life remain to be established.
How this may change practice
If validated, AI-driven personalized rehabilitation could transform vestibular therapy by enabling clinicians to deliver more precise, adaptive, and patient-centered interventions. This approach may reduce trial-and-error in exercise prescription, enhance patient motivation through tailored feedback, and improve functional outcomes.
Clinicians should remain informed about emerging AI tools and consider participation in research initiatives evaluating their utility in vestibular rehabilitation. Early adopters may explore integrating AI decision support systems and digital human twin platforms as adjuncts to standard care, while maintaining vigilance regarding ethical and practical challenges.
Ultimately, AI-enabled personalization holds promise to complement, not replace, clinical expertise, fostering a more nuanced understanding of vestibular dysfunction and recovery trajectories.