Machine Learning for Wound Management: A Structured Narrative Review of Applications, Challenges, and Prospects

Chronic and complex wounds represent a major clinical and economic burden, requiring frequent assessment, early detection of complications, and accurate prediction of healing outcomes. Conventional wound evaluation is often subjective and time-intensive, motivating the development of automated approaches. Machine learning (ML) has emerged as a powerful tool for wound management by enabling wound detection, segmentation, tissue characterisation, infection assessment, and healing prediction using wound images, clinical records, and sensor-derived data. This structured narrative review summarises key ML and deep learning (DL) methods applied in wound care, including commonly used architectures, data modalities, and evaluation strategies. We critically discuss persistent barriers limiting clinical adoption, such as limited dataset size and diversity, annotation inconsistency, poor generalizability, bias, lack of external and prospective validation, and challenges related to privacy, regulation, and clinical workflow integration. Finally, we highlight emerging opportunities including multimodal learning, self-supervised learning, federated learning, explainable AI (XAI), and mobile or wearable technologies for continuous wound monitoring. Overall, ML has strong potential to improve the objectivity, efficiency, and personalization of wound care; however, clinically deployable solutions will require standardised datasets, transparent reporting, rigorous validation, and interdisciplinary collaboration.