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Performance of artificial intelligence in diabetes-related foot ulcer detection and assessment: a scoping review of clinical validation studies
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DOI：10.1016/j.diabres.2026.113485.png)
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Abstract
En 中文
This scoping review synthesized clinical validation evidence of artificial intelligence (AI) algorithms for diabetes-related foot ulcer (DRFU) detection and assessment and identified factors influencing AI performance in real-world settings. A systematic search was conducted in PubMed, MEDLINE, CINAHL, Scopus, and Google Scholar, following the Arksey and O’Malley framework and reported using PRISMA-ScR. Eligible studies involved adult patients with diabetes and foot ulcers, utilized learning-based AI models, and reported clinical validation outcomes. Eleven studies published between 2020 and 2026 were included from eight countries. Diagnostic performance varied across studies, with sensitivity of 91–100%, specificity of 20–96.8%, and intraclass correlation coefficients of 0.825–0.998 for wound measurement reliability. AI systems reduced manual area overestimation by 13.4–25.2%. Influencing factors were mapped across three NASSS framework domains: technological factors including image quality and algorithmic misclassification, adopter-level barriers including digital literacy limitations, and organisational system-level constraints including infrastructure instability and data privacy concerns. These findings suggest early-stage evidence of promising AI diagnostic performance. However, the evidence base remains limited by small sample sizes, heterogeneous designs, and the inability of current systems to assess deeper wound features. Prospective multi-centre studies with standardised protocols are needed before routine clinical adoption can be recommended.
Keywords:
Deep learning
Human-AI comparison
Influencing factors
Machine learning
Metrics performance
Real-world implementation
AI
Artificial Intelligence
ARE
Absolute Relative Error
CNN
Convolutional Neural Network
DFU
Diabetic Foot Ulcer
DT-CNN
Discrete Time-Cellular Nonlinear Network
FN
False Negative
FP
False Positive
ICC
Intraclass Correlation Coefficient
NHS
National Health Service
NPV
Negative Predictive Value
PGT
Percent Granulation Tissue
PPV
Positive Predictive Value
SVM
Support Vector Machine
TN
True Negative
TP
True Positive
WBP
Wound Bed Preparation
Journal
IF:
7.4
Papers:
1.1W
Citations:
2.2W
Organization
Cited Papers