Machine learning in diabetic-foot osteomyelitis: current applications, challenges, and future directions

Background: Diabetic-foot osteomyelitis (DFO) is a severe complication of diabetic-foot ulcers associated with delayed healing, amputation, and increased mortality. Machine learning (ML) has increasingly been explored for diagnosis and clinical decision-making in diabetic-foot care; however, the current evidence specific to DFO has not been systematically synthesized. Methods: We performed systematic searches of MEDLINE, Embase, Scopus, and Web of Science for studies published between January 2000 and May 2026. Studies that developed or evaluated ML models for DFO diagnosis, prediction, or outcomes were included. Data extraction was performed using the CHARMS framework, reporting followed PRISMA 2020 and TRIPOD-SRMA recommendations, and methodological quality was evaluated using PROBAST $+$ AI. Due to considerable heterogeneity, narrative synthesis was performed. Results: A total of 10 studies published from 2022 to 2026 met the inclusion criteria. Five studies developed or evaluated ML models with a direct DFO diagnostic endpoint, one additional study evaluated model explainability, and four evaluated complementary DFO-relevant diabetic-foot applications. Reported discrimination ranged from 0.879 to 0.998, but comparisons were limited by differences in clinical endpoints, reference standards, input modalities and validation strategies. Only one direct DFO model was externally validated, and no study developed a multimodal model combining clinical and imaging data. Conclusion: Current evidence is limited by heterogeneous methodologies, limited external validation, and inconsistent reporting despite promising discrimination reported by several models. Future studies should evaluate whether multimodal models integrating clinical and imaging data provide incremental value over unimodal approaches, with external validation, calibration, and clinical utility assessment before clinical implementation.