Flexible wearable medical devices: from material innovations and data processing to intelligent healthcare applications.

BACKGROUND

Flexible wearable medical devices drive healthcare transformation via non-invasive, real-time physiological monitoring and personalized management. Traditional rigid devices lack long-term comfort, while chronic disease care and telemedicine demand reliable, patient-centered solutions. Advances in materials (carbon nanomaterials, liquid metals, hydrogels) enable stretchable, biocompatible substrates adapting to bodily movements. Innovative sensing (PPG, EEG, sweat-based detection) tracks blood pressure, glucose, and neural signals accurately. Key challenges include insufficient sensor flexibility, lagging supervision, data security issues, and large-scale production difficulties, requiring interdisciplinary collaboration.

AIM OF REVIEW: This review integrates cutting-edge advances in flexible wearable medical devices, covering material innovations, sensing breakthroughs, power management, and AI-driven data analysis. It evaluates strategies to enhance biocompatibility of carbon composites, liquid metals, and hydrogels, analyzing improvements in device sensitivity, biocompatibility, and real-world applicability. It critically discusses machine learning's role and limitations in chronic disease management, telemedicine, and sports optimization. Key unresolved challenges include lagging regulations, privacy risks in multi-source data fusion, mismatched sensing development, and large-scale production difficulties. The aim is to provide a scalable, secure, and clinically translatable roadmap for future research.

KEY SCIENTIFIC CONCEPTS OF REVIEW: Core technologies involve carbon nanomaterials (e.g., graphene), liquid metals (structurally optimized for balanced conductivity and flexibility), and hydrogels (preferred as flexible substrates for mimicking natural soft tissues' mechanics and biocompatibility). Sensing technologies (PPG, EEG, sweat-based detection) enable non-invasive, real-time monitoring of vital parameters like blood pressure, glucose, and neural signals. AI decodes complex physiological data for predictive diagnosis, while IoT/5G integration advances telemedicine. Current limitations include poor sensor flexibility, lagging regulations, and large-scale production hurdles, contrasting with trends like advanced sensing, new materials, and hybrid diagnostic-therapeutic systems-highlighting flexible wearables' potential in intelligent healthcare.