Diabetic wounds, particularly diabetic foot ulcers, are among the most debilitating complications of diabetes mellitus, often leading to chronic infection, poor quality of life, and even limb amputation. The wound healing process in diabetic individuals is profoundly impaired due to prolonged hyperglycaemia, vascular insufficiency, neuropathy, and an altered immune response. These factors contribute to delayed healing, increased risk of infection, and elevated healthcare costs. Recent advances in Artificial Intelligence (AI) have introduced novel opportunities to address the multifaceted challenges of diabetic wound care. AI-powered tools can facilitate early detection, wound classification, risk stratification, personalized treatment planning, and continuous remote monitoring. Technologies such as deep learning, computer vision, and predictive modelling are being integrated into smartphone apps, wearable devices, and clinical decision support systems to enhance both diagnosis and therapeutic outcomes. Despite promising progress, limitations including data quality, algorithm transparency, and clinical validation remain significant barriers. This review explores the pathophysiology of diabetic wound healing, the clinical complications, and the emerging role of AI in revolutionizing wound care, while highlighting current limitations and future directions for research and implementation

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