Integrating AI-driven wearable devices and biometric data into stroke risk assessment: A review of opportunities and...

Stroke is a leading cause of morbidity and mortality worldwide, and early detection of risk factors is critical for prevention and improved outcomes. Traditional stroke risk assessments, relying on sporadic clinical visits, fail to capture dynamic changes in risk factors such as hypertension and atrial fibrillation (AF). Wearable technology (devices), combined with biometric data analysis, offers a transformative approach by enabling continuous monitoring of physiological parameters. This narrative review was conducted using a systematic approach to identify and analyze peer-reviewed articles, reports, and case studies from reputable scientific databases. The search strategy focused on articles published between 2010 till date using pre-determined keywords. Relevant studies were selected based on their focus on wearable devices and AI-driven technologies in stroke prevention, diagnosis, and rehabilitation. The selected literature was categorized thematically to explore applications, opportunities, challenges, and future directions. The review explores the current landscape of wearable devices in stroke risk assessment, focusing on their role in early detection, personalized care, and integration into clinical practice. The review highlights the opportunities presented by continuous monitoring and predictive analytics, where AI-driven algorithms can analyze biometric data to provide tailored interventions. Personalized stroke risk assessments, powered by machine learning, enable dynamic and individualized care plans. Furthermore, the integration of wearable technology with telemedicine facilitates remote patient monitoring and rehabilitation, particularly in underserved areas. Despite these advances, challenges remain. Issues such as data accuracy, privacy concerns, and the integration of wearables into healthcare systems must be addressed to fully realize their potential. As wearable technology evolves, its application in stroke care could revolutionize prevention, diagnosis, and rehabilitation, improving patient outcomes and reducing the global burden of stroke.