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

Authors

Hongbin Sun, Xilin Liu, Hong Li

Published in

Frontiers in nutrition. Volume 13. Pages 1821103. Epub Jun 17, 2026.

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.

PMID:
42389702
Bibliographic data and abstract were imported from PubMed on 02 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.