Wearable Devices and AI-Driven Remote Monitoring in Cardiovascular Medicine: A Narrative Review

AI-integrated wearable devices (WDs) offer real-time cardiovascular disease (CVD) monitoring with the potential to improve early detection and patient-centred care. However, the breadth, quality, and clinical applicability of supporting evidence remain variable.

A narrative review was conducted of peer-reviewed original studies, clinical trials, reviews, and regulatory documents published in English from January 2013 to June 2025. A structured Boolean search identified literature on AI-enabled WDs for cardiovascular monitoring. Selection criteria prioritised randomised trials, prospective observational studies, and real-world implementation reports with a clear regulatory context. Studies were narratively analysed for device classification, physiologic parameters measured, AI techniques, clinical validation outcomes, and evidence of patient engagement.

Evidence demonstrates high diagnostic accuracy for arrhythmia detection and promising outcomes in heart failure monitoring, particularly with Food and Drug Administration or Conformité Européenne-approved devices. Nonetheless, most studies were small, short-term, and conducted in controlled settings, limiting generalisability. Key gaps include underrepresentation of diverse populations, lack of interoperability with electronic health records, limited AI explainability, and incomplete cost-effectiveness evaluation. Patient engagement was inconsistently addressed, with most reporting only basic usability or satisfaction measures rather than co-design or adherence optimisation strategies.

AI-integrated WDs show considerable promise in enhancing CVD detection and management but require large-scale pragmatic trials, standardised interoperability protocols, transparent AI model interpretability, and structured patient involvement. Addressing these gaps will be essential for equitable, scalable, and clinically integrated adoption.