Diabetic foot complications represent a major global health challenge, with a high prevalence among patients with diabetes. A diabetic foot ulcer (DFU) not only affects the patient’s quality of life but also increases the risk for amputation. Worldwide, a DFU occurs every second, and an amputation occurs every 20 seconds. The limitations of current detection and intervention methods underline the urgent need for innovative solutions.
However, recent advances in artificial intelligence (AI) have paved the way for individualized risk prediction models for chronic wound management. These models use deep learning algorithms to analyse clinical data and images, providing personalized treatment plans that may improve healing outcomes and reduce the risk for amputation.
AI-powered tools can also be deployed for the diagnosis of diabetic foot complications. Using image analysis and pattern recognition, AI tools are learning to accurately detect signs of DFUs and other complications, facilitating early and effective intervention and helping to create an automated “foot selfie” to predict and prevent problems before they start.
Although the potential benefits of AI in diabetic foot care are immense, integrating these tools into clinical practice poses challenges. These include ensuring the reliability of AI predictions, addressing data privacy concerns, and training healthcare professionals on the use of AI technologies.
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