Electrochemical and Surface-Enhanced Raman Spectroscopy-Based Biosensing Strategies for Wound Infection Detection: Recent Advances and Applications in Point-of-Care Diagnostics

The growing prevalence of chronic wound infections presents a major global healthcare challenge due to delayed healing, prolonged hospitalization, and increased rates of limb amputation. Conventional diagnostic approaches, including clinical assessment, microbiological culture, and laboratory-based analyses, are often limited by subjective interpretation, delayed turnaround times, invasive sampling, and an inability to continuously monitor the dynamic wound microenvironment. Because wound infection involves changes in both host physiology and microbial activity, reliance on a single biomarker may not adequately characterize wound status, highlighting the potential of multimodal point-of-care diagnostic platforms. Recent advances in wearable biosensors, surface-enhanced Raman spectroscopy (SERS), nanomaterials, and artificial intelligence (AI) have supported the development of multimodal approaches for monitoring complementary host- and pathogen-derived biomarkers to facilitate rapid, non-invasive, and continuous wound infection assessment. This review examines selected multimodal biosensing approaches for wound infection detection, with a focus on electrochemical biosensors, SERS, and AI-assisted data analytics within point-of-care diagnostic frameworks. The review discusses (1) key wound infection biomarkers and the fundamental principles of electrochemical and SERS-based sensing; (2) recent developments in multimodal biosensing platforms, advanced plasmonic materials, and AI-assisted data interpretation and multimodal data fusion; and (3) current challenges and future opportunities for the clinical translation of point-of-care wound diagnostics. By combining complementary sensing modalities with AI-driven data analytics, these approaches may support earlier infection detection, continuous non-invasive monitoring, personalized wound management, and more informed clinical decision-making.