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Artificial Intelligence-Enhanced Wearable Blood Pressure Monitoring in Resource-Limited Settings: A Co-Design of Sensors, Model, and Deployment

Authors: Yiming Zhang; Shirong Qiu; Kai Du; Shun Wu; Ting Xiang; Kenghao Zheng; Zijun Liu; Hanjie Chen; Nan Ji; Fa Wang; Weijia Wu; Yuan-Ting Zhang

DOI: 10.1007/s40820-025-02003-9Status: Verified Translated Edition
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Key Findings in This Report

• Integrative Co-Design Framework: Synthesizes advances in sensing, models, accuracy/reliability assessment, and hardware into a sensor–model–deployment–assessment framework, balancing precision and efficiency for cuffless BP monitoring. • Pathways to Clinical Translation: Critically assesses barriers to real-world deployment, offering actionable strategies to bridge the translational gap for scalable implementation in low-resource regions. • Interdisciplinary Synthesis: Integrates materials science, digital health, and embedded AI to provide evidence-based recommendations for equitable diagnostic solutions. • Global Health Equity: Emphasizes potential of AI-enhanced wearable BP monitoring to support proactive hypertension control and promote cardiovascular health equity worldwide.