Markerless Motion Analysis and AI-Based Platforms for Neurological Telerehabilitation: A Scoping Review

BackgroundTelerehabilitation (TR) delivers rehabilitation services through digital and information technologies. Recent advances in artificial intelligence (AI) have introduced new opportunities for TR, particularly in remote monitoring and individualized treatment. This scoping review aims to examine and synthesize the current literature on the use of AI and markerless motion analysis (MMA) within TR for patients with neurological disorders, distinguishing between approaches focused on remote monitoring/assessment and those supporting AI-based TR platforms.MethodsA scoping search conducted in March 2025 identified articles published in the last ten years in the following databases: PubMed, Embase, Scopus and Web of Science (WoS).ResultsThe initial search retrieved 290 records. After removing 67 duplicates, the remaining records were screened. Following full-text assessment, only 10 studies were included, while 208 were excluded due to wrong population (n = 93), study design (n = 89), outcomes (n = 21), or language (n = 5). Overall, the evidence for both MMA-based remote monitoring/assessment and AI-supported TR interventions remains early-stage and heterogeneous across populations, outcomes, and set-ups.ConclusionAI applications in TR and remote monitoring for neurological disorders remain early stage and heterogeneous. While current platforms remain largely experimental, AI-based TR devices and metrics can offer objective, quantitative data to support personalized care, reinforcing the essential role of remote rehabilitation and monitoring in maintaining the patient-clinician connection. Therefore, integrating AI to promote continuity of rehabilitation beyond the clinic may provide a novel way to tailor treatment intensity, adapt exercises over time, and optimize follow-up in neurological rehabilitation.Registration number10.17605/OSF.IO/FB8TD.