AI-Powered Wearable Sensors for Health Monitoring and Clinical Decision Making

AI-powered wearable sensors represent a transformative frontier in remote health monitoring, offering unprecedented opportunities for real-time diagnostics, personalized interventions, and proactive disease management. This review synthesizes advancements in AI-integrated biosensor technologies, exploring their applications across diverse health conditions—including diabetes, cardiovascular diseases, neurodegenerative disorders, mental health, and maternal/neonatal care—while addressing critical challenges in scalability, privacy, and robustness. By analyzing peer-reviewed studies from 2014 onward, we highlight how machine learning algorithms, such as federated learning, transfer learning, and edge-AI, enhance the precision of wearable devices in processing physiological signals like glucose levels, gait patterns, and heart rate variability. Key innovations, such as continuous glucose monitors (CGMs) with FDA-approved over-the-counter accessibility and AI-driven digital twins for predictive health modeling, underscore the shift toward patient-centric care. However, persistent gaps remain, including sensor-device heterogeneity, data privacy concerns, and the need for adaptive models to address distribution shifts across populations. We further discuss emerging paradigms like large language models (LLMs) for contextualized health insights and counterfactual explanations for transparent decision-making. By bridging technical advancements with clinical needs, this review charts a roadmap for the future of AI-powered wearables, emphasizing their potential to democratize healthcare access, reduce disparities, and enable precision medicine on a global scale. Through an in-depth analysis of methodologies, biomarkers, and emerging trends, this review provides a comprehensive perspective on how AI-powered biosensors are paving the way for precision medicine, adaptive interventions, and more effective disease management.