An explainable artificial intelligence system for prognostic assessment and decision support in diabetic foot ulcer care: a multicentre derivation and validation study with prospective randomised evaluation

Background Diabetic foot ulcers (DFUs) carry risks of recurrence, amputation, and all-cause mortality, but most prediction models address single outcomes and have limited interpretability. We developed an interpretable multi-outcome machine-learning framework for individualised DFU risk assessment and prospective clinical evaluation. Methods We conducted a development and validation study across three hospitals in China from January 1, 2018, to April 30, 2025. We used electronic medical records and follow-up data from 763 patients at Centres A and B to develop and internally validate an ensemble of single-task extreme gradient boosting (ST-XGBoost) and single-task multilayer perceptron (ST-MLP) models to predict cure, recurrence, amputation, and all-cause mortality. The locked ensemble was externally validated in 100 patients at Centre C. Model performance was assessed using discrimination, calibration, and decision curve analysis, with interpretability evaluated using Shapley additive explanations (SHAP). From January 5 to March 5, 2026, we performed a prospective comparative evaluation. We randomised 202 participants (1:1) to usual care or artificial intelligence (AI)-assisted care, of whom 200 were analysed. We compared AI-assisted physician decision-making via an evolvable web-based online prediction platform versus usual care. The outcome was overall quality score of the final clinical recommendation. The platform supports follow-up data collection for future offline model updating. Findings In the training dataset, cure, recurrence, amputation, and all-cause mortality occurred in 638, 208, 226, and 167 patients, respectively; the corresponding numbers in the external validation dataset were 80, 34, 24, and 25. In independent external validation, the ST-XGBoost + ST-MLP ensemble outperformed six comparators, with a macro-averaged AUC of 0.901, accuracy of 0.875, and F1 score of 0.830. Outcome-specific discrimination was highest for amputation (AUC 0.953) and all-cause mortality (AUC 0.951), followed by cure (AUC 0.880) and recurrence (AUC 0.820). In the prospective evaluation, AI-assisted recommendations received higher scores than corresponding clinician recommendations (7.77 vs 6.47; mean difference 1.30, 95% confidence interval (CI) 1.08–1.52; p < 0.0001), with the largest gain in completeness. The AI-assisted group showed better short-term follow-up engagement and glucose-management adherence. Interpretation We demonstrate that integrating gradient-boosted trees and neural networks enables explainable prediction of complex DFU trajectories and may support prioritised, individualised clinical decision-making. Nevertheless, recurrence estimates should be interpreted cautiously.