Distributed Precision Stroke Care: Artificial Intelligence-Driven Stroke Management Using Multimodal Sensor Data

Stroke. 2026 Feb;57(2):526-537. doi: 10.1161/STROKEAHA.125.050447. Epub 2025 Oct 22.

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.

Keywords: artificial intelligence; atrial fibrillation; community healthcare; digital health; precision medicine; smartphone.

Publication types

  • Review

MeSH terms

  • Artificial Intelligence*
  • Humans
  • Precision Medicine* / methods
  • Stroke Rehabilitation* / methods
  • Stroke* / diagnosis
  • Stroke* / therapy
  • Telemedicine
  • Wearable Electronic Devices