Artificial Intelligence-Based Models for Diabetic Foot Ulcer Assessment : A Scoping Review (Preprint)
Bibliographic record
Abstract
Background: Diabetic foot ulcers (DFU) are serious complications of diabetes that contribute substantially to morbidity, mortality, and health care burden. Accurate and timely wound assessment is essential for effective DFU management; however, conventional assessment methods are limited by subjectivity, time constraints, and interobserver variability. Objective: This scoping review aimed to map and synthesize evidence regarding the development and application of artificial intelligence (AI)-based models for DFU assessment. Methods: A scoping review was conducted following the Arksey and O'Malley framework and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Literature searches were performed in PubMed, ProQuest, and Scopus for studies published between 2014 and 2026. Study selection and data charting were conducted independently by two reviewers using predefined inclusion criteria based on the PCC (population, concept, context) framework. Extracted data were synthesized narratively and categorized according to major AI application domains. Results: A total of 654 records were identified, of which 46 studies met the inclusion criteria. The included studies predominantly focused on image segmentation, diagnostic classification, and risk prediction or monitoring of DFUs. Convolutional neural networks were the most commonly applied models, with performance evaluated using metrics such as accuracy, Dice similarity coefficient, and area under the curve. Most studies relied on retrospective, single-center datasets, with limited external validation and minimal real-world clinical implementation. Conclusions: AI-based models demonstrate strong potential to enhance DFU assessment and monitoring by improving accuracy and efficiency. However, significant gaps remain in terms of dataset diversity, external validation, and integration into clinical workflows. Future research should prioritize prospective validation, standardized datasets, and real-world implementation to support safe and effective clinical adoption.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".