Artificial Intelligence In Wound Care And Diabetic Foot Management

By Dr Ahmed Elawadi BVSc IIWCC LM101 LM201

DOI: 10.56885/188551frniwd

Chronic wounds, particularly diabetic foot ulcers (DFUs), continue to represent a significant global healthcare burden due to their high prevalence, recurrence rates, prolonged healing times, and associated risk of hospitalization and amputation. Traditional wound assessment approaches rely heavily on subjective clinical evaluation, manual documentation, and periodic monitoring, resulting in variability in care and delayed intervention. Artificial intelligence (AI) has emerged as a transformative tool capable of improving multiple aspects of wound care, including screening, diagnosis, wound measurement, healing prediction, remote monitoring, and clinical decision support. Recent advances in machine learning, deep learning, thermal imaging, and computer vision have demonstrated strong potential for improving early detection and standardization in diabetic foot management. AI-driven systems can analyze wound images, identify tissue characteristics, monitor progression, and predict healing outcomes with diagnostic accuracies approaching expert clinician performance in controlled environments. Furthermore, AI integration with telemedicine and smartphone-based wound monitoring may expand access to specialized care in underserved regions. This article reviews the current role of AI in wound care and diabetic foot management, examines recent scientific evidence, discusses clinical applications and limitations, and explores the future direction of predictive and preventive wound care models.