Accepted for/Published in: JMIR Diabetes
Date Submitted: May 22, 2025
Open Peer Review Period: Jun 6, 2025 - Aug 1, 2025
Date Accepted: Apr 29, 2026
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Application of Artificial Intelligence-Based Assessment Models in Evaluating Diabetic Foot Ulcers: A Scoping Review
ABSTRACT
Background:
Diabetic foot ulcers (DFU) represent a severe complication that can increase morbidity and mortality in diabetic patients. Effective management of DFU requires accurate and prompt wound assessment. However, the need for proper management of DFU necessitates wound assessments that are both swift and accurate, a challenge that persists in current clinical practice.
Objective:
This study explores the application of AI-based assessment models in evaluating DFU conditions, aiming to enhance detection accuracy, transparency in medical decision-making, and the effectiveness of real-time patient monitoring and care.
Methods:
A scoping review methodology based on the PRISMA-ScR framework was used to identify, select, and summarize literature on the use of AI in DFU assessment. Literature was sourced from PubMed, ProQuest, and Scopus using keywords like diabetic foot ulcer, Artificial Intelligence, and wound assessment.
Results:
AI models demonstrate high accuracy in risk prediction, detection, segmentation, and classification of diabetic foot ulcers (DFU), with some models achieving up to 99% accuracy. Smart applications and deep learning-based systems have proven to be reliable and comparable to clinical evaluations, enhancing efficiency and transparency in DFU management.
Conclusions:
The development and application of AI-based models in DFU assessment and monitoring improve diagnostic effectiveness and accuracy while supporting more transparent and timely medical decisions.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.