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

Abstract

Highlights

Integrative Co-Design Framework: We synthesize current advances in sensing, models, accuracy/reliability assessment, and hardware into a sensor–model–deployment–assessment framework that organizes evidence and design trade-offs for cuffless blood pressure monitoring. The framework seeks to balance precision and efficiency by jointly considering low-power edge AI, streamlined sensor architectures, and adaptive computational models, providing a structured basis for reproducible and clinically meaningful wearable solutions.

Pathways to Clinical Translation: We critically assess barriers to real-world deployment, offering actionable strategies to bridge the translational gap between laboratory innovations and scalable implementation in low-resource regions with minimal healthcare infrastructure.

Interdisciplinary Synthesis: By integrating cutting-edge advances in materials science, digital health, and embedded AI, we provide evidence-based recommendations to empower biomedical researchers, engineers, and data scientists in advancing equitable diagnostic solutions.

Accurate blood pressure (BP) monitoring is essential for preventing and managing cardiovascular disease. Advancements in materials science, medicine, flexible electronic, and artificial intelligence (AI) have enabled cuffless, unobtrusive BP monitoring systems, offering an alternative to traditional sphygmomanometers. However, extending these advances to real-world cardiovascular care particularly in resource-limited settings remains challenging due to constraints in computational resources, power efficiency, and deployment scalability. This review presents a comprehensive synthesis of AI-enhanced wearable BP monitoring, emphasizing its potential for personalized, scalable, and accessible healthcare. We systematically analyze the end-to-end system architecture, from mechano-electric sensing principles and AI-based estimation models to edge-aware deployment strategies tailored for low-resource environments. We further discuss clinical validation metrics and implementation barriers and prospective strategies. To bridge lab-to-field translation, we propose an innovative "sensor-model-deployment-assessment" co-design framework. This roadmap highlights how AI-enhanced BP technologies can support proactive hypertension control and promote cardiovascular health equity on a global scale.