Diagnostic accuracy of smartphone and artificial intelligence tools for diabetic foot screening

Oscar Alfredo Cabrera, Oscaracabrera@yahoo.com.ar, Francis Feldick, Fafeldick@gmail.com, Sheila Ailin Cabrera, Sheilacabrera2304@icloud.com, Erica Cecilia Delbon and Chechudel@gmail.com

SAP Podiatry, 2026

Abstract: Introduction: Diabetic foot ulcers precede most diabetes-related lower-limb amputations, and early detection depends on specialist clinical assessment that is not always available. Smartphone-based and artificial intelligence image tools could extend screening, but their diagnostic accuracy remains scattered across heterogeneous studies. Methods: A systematic review; the bibliographic databases consulted were Scopus, Pubmed, Google Scholar, Open Alex and Crossref. Results: From 963 records, 19 studies were included, 8 of smartphone-based tools and 11 of artificial intelligence models. A smartphone artificial intelligence system detected ulcers with 0.9% sensitivity and 0.9% specificity against expert judgement. Deep learning models reported accuracy between 78.3% and 99.9%, sensitivity between 95.2% and 99.9%, and specificity between 94.0% and 99.9%. Patient selection was at high risk of bias in 15 of 19 studies. Conclusions: Smartphone and artificial intelligence tools reach high diagnostic accuracy for diabetic foot screening, but the evidence derives mostly from retrospective image datasets and requires prospective external validation before clinical use.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:cwf:podart:pod2026260

DOI: 10.62486/pod2026260

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