Flexible Electrochemical Sensors: Emerging Biomedical Applications and Future Perspectives
Flexible electrochemical sensors have emerged as a promising platform for real-time, noninvasive health monitoring, driven by rapid advances in nanomaterials, fabrication methods, and system integration. Over the past decade, substantial efforts have focused on improving their mechanical flexibility, analytical sensitivity, and biocompatibility, thereby expanding their potential in clinical diagnostics and personalized healthcare. Despite this progress, several key challenges continue to hinder practical translation. This review provides a comprehensive overview of recent advances in flexible electrochemical sensors, with particular emphasis on nanomaterial engineering, device fabrication strategies, and biomedical applications. Representative applications in sweat, interstitial fluid, tears, and saliva are summarized and critically discussed. The review also examines current limitations, including challenges in sample collection, the variable reliability of biofluid biomarkers, and interference from complex biological matrices. Finally, future perspectives are presented on the integration of artificial intelligence and machine learning with flexible electrochemical sensors to enable improved data interpretation, interference correction, and more accurate biomarker identification in complex biofluids. Recent advances in flexible electrochemical biosensors were summarized. Sweat, ISF, saliva, and tear sensing performance was critically discussed. Nanomaterial strategies for enhanced sensitivity and flexibility were reviewed. 3D-printing for scalable and customizable sensor platforms was highlighted. AI-assisted analysis for biomarkers classification was discussed. Recent advances in flexible electrochemical biosensors were summarized. Sweat, ISF, saliva, and tear sensing performance was critically discussed. Nanomaterial strategies for enhanced sensitivity and flexibility were reviewed. 3D-printing for scalable and customizable sensor platforms was highlighted. AI-assisted analysis for biomarkers classification was discussed.