Research Progress on Artificial Intelligence in the Management of Diabetic Foot Ulcers.

Authors

Jinxia Du, Xiaoli Liu, Guihui Wu, Yin Huang, Yan Zhang, Xiang Long, Xiaoyi Yang, Yan Zhou

Published in

Diabetes, metabolic syndrome and obesity : targets and therapy. Volume 19. Pages 637013. Epub Oct 07, 2026.

Abstract

As a typical chronic complication of diabetes, the management of diabetic foot ulcers has long faced multiple challenges, including the highly subjective nature of wound assessment, patients'limited capacity for self-care at home, and the difficulty in predicting healing outcomes. The emergence of artificial intelligence (AI) technology offers new approaches to addressing these challenges. Based on recent domestic and international literature, this paper systematically reviews the research progress of artificial intelligence in the care of diabetic foot ulcers, focusing on four key dimensions: intelligent wound assessment (eg deep learning-based image analysis models achieving an F1 score of 93.1% for infection identification and 92.8% accuracy for area assessment), patient self-health management (eg mobile health applications and wearable monitoring devices), the development and application of smart dressings (eg responsive biomaterials and sensor integration), and the prediction of healing trends (eg machine learning prediction models, with support vector machines achieving 85.3% accuracy and an AUC of 0.922 in predicting complete DFU healing). It summarizes key technological achievements and the current status of clinical translation, provides an in-depth analysis of existing shortcomings (including insufficient algorithm generalizability, lack of prospective clinical validation, and absence of data standardization), and outlines future development directions (encompassing multimodal integration, explainable artificial intelligence, and implementation science research), with the aim of offering guidance for the further application of artificial intelligence technology in the field of chronic wound care.

PMID:
42861308
Bibliographic data and abstract were imported from PubMed on 12 Oct 2026.

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