Precision nutrition in diabetic foot ulcers: multimodal artificial intelligence for personalized metabolic management

Abstract

This review synthesizes AI applications in diabetic foot ulcer (DFU) management, with a particular focus on nutritional and metabolic data integration. Emerging AI methodologies—including image-based dietary assessment, natural language processing-driven chatbots, and continuous glucose monitoring-integrated predictive models—have shown promise in adjacent fields such as general type 2 diabetes management and hemodialysis. However, none have been directly validated in DFU populations, and their applicability to DFU care remains a future research direction rather than a current reality. The main obstacles include the paucity of standardized nutritional data in existing DFU cohorts, methodological barriers in multi-modal data fusion, and the need for robust validation across diverse populations. A future research agenda is proposed, emphasizing the convergence of AI, nutritional science, and multidisciplinary care pathways. By addressing these foundational gaps, AI-enabled approaches may eventually contribute to reducing the global burden of diabetes-related amputations, but substantial methodological and validation work is required before clinical translation can be realistically anticipated.