Performance of artificial intelligence in diabetes-related foot ulcer detection and assessment: a scoping review of clinical validation studies.

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.