Tele-Neurology Meets Artificial Intelligence: Current Progress, Limitations, and Emerging Horizons

Access to neurological care remains profoundly unequal worldwide, driven by the increasing burden of chronic and neurodegenerative disorders and a persistent shortage of specialist neurologists. Tele-neurology has emerged as a promising strategy to improve access to neurological expertise, while recent advances in artificial intelligence (AI) have expanded its capabilities beyond remote consultation toward data-driven diagnosis, monitoring, and clinical decision support. This narrative review critically synthesizes current evidence on the evolution of tele-neurology and the emerging role of AI across multiple domains of neurological care. The review examines AI-enhanced diagnostic applications, including neuroimaging, electroencephalography, and digital biomarkers, as well as AI-assisted remote monitoring, patient engagement, and predictive analytics in disorders such as stroke, epilepsy, Parkinson’s disease, multiple sclerosis, dementia, headache disorders, and neuromuscular diseases. Available evidence suggests that tele-neurology can achieve clinical outcomes comparable to in-person care in selected settings while improving accessibility, continuity of care, and patient satisfaction. AI applications have shown promising early results in image interpretation, automated EEG analysis, remote disease monitoring, and individualized risk prediction. Despite these advances, important challenges remain. Limitations include constraints in remote neurological examination, evidence gaps regarding clinical validation, unequal access to digital infrastructure, data governance and cybersecurity concerns, fragmented regulatory frameworks, and difficulties integrating AI into routine clinical workflows. Furthermore, the clinical maturity of AI applications varies substantially, with many systems remaining at the proof-of-concept or early validation stage. To make this variation explicit, we introduce a transparent, criteria-based three-tier classification of the clinical practicability of AI applications, graded by strength of evidence, degree of validation, and real-world integration. Tele-neurology is increasingly evolving toward hybrid models that combine in-person neurological assessment with digitally enabled longitudinal monitoring and AI-supported decision tools. Future progress will depend on rigorous clinical validation, equitable implementation strategies, integration into healthcare systems, and the development of multimodal AI approaches that combine clinical, imaging, electrophysiological, and digital biomarker data to support personalized neurological care.