AI-integrated smartwatch monitoring for early detection of stroke and hemorrhage: A systematic review

Background: Early detection of cerebrovascular events (subarachnoid hemorrhage [SAH], ischemic stroke, intracranial hemorrhage [ICH]) is critical for timely intervention. Emerging smartwatches with multiple sensors and artificial intelligence (AI) algorithms offer potential for real-time detection of acute neurological events. We performed a systematic review (2010–2025) per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines to identify published studies on AI-enabled smartwatches for detecting SAH, ischemic stroke, and ICH. Methods: We searched multiple databases from January 2010 through May 2025 using combinations of smartwatch OR wearable , stroke OR SAH OR ICH , and AI OR machine learning OR deep learning . Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool for diagnostic accuracy studies. Results: The search yielded 3 eligible studies on ischemic stroke detection by wearable accelerometers with AI. The stroke studies (n = 3) used bilateral wrist/arm accelerometers to detect unilateral motor deficits. Deep learning models achieved high diagnostic accuracy (area under the receiver operating characteristic curve 0.95–0.99) for detecting acute stroke symptoms. One study reported a median detection time of 15 to 29 minutes after stroke onset, depending on the false alarm threshold. A feasibility trial (STROKE ALARM) using accelerometer bands and a smartphone app triggered frequent false alarms without observed strokes. Conclusion: Wearable technology with AI shows promise for ischemic stroke symptom detection, but there is a critical gap in SAH and ICH detection. Challenges include sensor accuracy, false alarms, and algorithm generalizability. We propose a conceptual multisensory model integrating heart rate (electrocardiogram/photoplethysmography), blood pressure surrogates, and motion data into an AI pipeline for future smartwatch systems, which can lead to the detection of stroke/SAH, might show promise in other brain pathologies.