Distributed Precision Stroke Care: Artificial Intelligence-Driven Stroke Management Using Multimodal Sensor Data
Aline F Pedroso, Lee H. Schwamm, Rohan Khera
Abstract
Delays in stroke diagnosis contribute to long-term disability. Many patients still face barriers to effective risk factor management, timely detection, and access to poststroke rehabilitation. The emergence of artificial intelligence-enabled, consumer-facing health technologies offers a transformative opportunity to address these gaps across the stroke care continuum. This review examines the evolving role of artificial intelligence-powered devices, including smartwatches, smartphones, wearable sensors, and ambient home-based technologies, in enabling precision stroke care. For stroke prevention, these tools facilitate scalable monitoring of cardiometabolic and stroke-specific risk factors. For early detection, artificial intelligence algorithms applied to multimodal sensor data can identify subtle neurological impairments and support real-time triage. In recovery, artificial intelligence-enhanced remote monitoring and virtual supervision offer scalable models for delivering personalized rehabilitation outside of specialized centers. Although most of these innovations remain in early development, they signal a paradigm shift toward accessible, individualized, and data-driven stroke prevention and management.