Polymeric biomaterials, particularly electrospun nanofibers, are increasingly central to the development of advanced wound dressings capable of supporting tissue regeneration while enabling real-time physiological monitoring. Chronic wounds associated with diabetes, vascular diseases, and cancer require continuous and personalized management, prompting the convergence of electrospun polymeric scaffolds with wearable biosensors and artificial intelligence (AI). These next-generation smart wound dressings utilize biocompatible polymer matrices functionalized with responsive sensing elements to monitor pH, temperature, moisture, oxygen saturation, and inflammatory biomarkers in situ. Molecular-level interactions between polymeric components and biological tissues facilitate both therapeutic delivery and diagnostic functionality. AI, including deep and federated learning, enhances these systems by enabling data-driven prediction of healing trajectories and personalized interventions. Key advances in flexible electronics, self-powered systems, and closed-loop feedback mechanisms further enhance clinical applicability. However, challenges remain, including the biochemical stability of sensors in enzyme-rich environments, secure wireless communication, and the lack of standardized datasets and clinical validation frameworks. This review critically examines recent progress in AI-integrated polymeric wound care systems, emphasizing the design of functional polymeric scaffolds, biosensor-polymer interfaces, and future directions, including biosensor miniaturization, multi-omics data integration, and scalable cloud-based platforms. A collaborative roadmap is proposed to advance these intelligent biomaterial systems toward clinical translation in chronic wound care.
Palani et al. (2025) studied this question.