Abstract

Background: Tele-neurodiagnostics has emerged as a transformative paradigm integrating telemedicine, Artificial Intelligence (AI), robotics, digital biomarkers and portable diagnostic technologies into neurological care. The rapid evolution of remote neurodiagnostic systems has accelerated particularly in stroke medicine, where timely diagnosis and intervention remain critically associated with functional outcomes and mortality reduction.
Objective: To systematically evaluate the clinical effectiveness, technological architecture and implementation challenges of tele-neurodiagnostic systems in neurological disorders, with particular emphasis on AI-assisted stroke diagnostics, robotic neurological assessment and digital neurovascular monitoring.
Methods: A PRISMA-compliant systematic review was conducted using PubMed/MEDLINE, Scopus, Web of Science and Cochrane Library databases. Studies published between January 2014 and March 2026 evaluating tele-neurodiagnostic interventions, AI-assisted neurological diagnostics, robotic assessment systems and remote neurological monitoring were included. Randomized controlled trials, prospective observational studies, multicenter cohort analyses and systematic reviews were eligible. Risk of bias was assessed using Cochrane RoB 2, ROBINS-I and QUADAS-2 instruments.
Result: A total of 118 studies met inclusion criteria. Tele-neurodiagnostic systems demonstrated substantial effectiveness in acute stroke triage, remote EEG interpretation, AI-assisted neuroimaging analysis and wearable neurological monitoring. AI-based large vessel occlusion detection achieved sensitivities between 82% and 96%, while tele-stroke systems reduced door-to-needle times by 18- 42 minutes. Portable neurodiagnostic devices improved access in rural and resource-limited settings. Robotic neurological examination systems and digital biomarkers demonstrated increasing diagnostic precision across stroke, Parkinson’s disease, epilepsy and neurodegenerative disorders. Integrated AIneurovascular platforms improved triage efficiency, workflow automation and prediction of functional outcomes.
Conclusion: Tele-neurodiagnostics represents a paradigm shift from episodic institution-based neurology toward continuous, intelligent and distributed neurological care. Stroke medicine has emerged as the leading field driving implementation of AI-assisted tele-neurology systems. Future directions include multimodal AI integration, autonomous neurovascular triage, federated learning architectures, digital twin modeling and large-scale validation of remote neurological diagnostics.

Keywords:Tele-neurodiagnostics; Stroke; Artificial intelligence; Tele-stroke; Digital neurology; Neuroimaging; Remote EEG; Robotics; Digital biomarkers; Wearable neurology; AI-assisted diagnostics; Portable neurodiagnostics