Explainable artificial intelligence in urolithiasis: Applications and future directions

Objective Artificial intelligence has demonstrated strong performance in urolithiasis detection, treatment planning, and prognosis. However, most systems operate as opaque “black boxes”, limiting trust, adoption, and regulatory approval. This review focuses specifically on explainable artificial intelligence (XAI) in urolithiasis, highlighting how interpretability methods strengthen clinical confidence, enable patient communication, and satisfy regulatory demands. Methods A PubMed search was conducted up to September 2025 for studies applying XAI methods such as Shapley Additive Explanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Gradient-weighted Class Activation Mapping (Grad-CAM) in the diagnosis, treatment, or prognosis of urinary stone disease. Eligible studies included original human research integrating explainability into artificial intelligence models. Results Across surgical outcome prediction, complication modeling, extracorporeal shock wave lithotripsy, diagnostic imaging, laboratory analysis, and recurrence risk, XAI consistently identified clinically recognizable features such as stone size, Hounsfield units, urine culture results, and anatomical parameters as key drivers of prediction. Visual tools such as heatmaps and relevance maps confirmed that deep learning models attend to clinically plausible regions, improving transparency. XAI also enhanced quality assurance in laboratory stone analysis and facilitated patient-centered decision-making. Conclusion This article centers on explainability as the decisive factor for clinical adoption. By linking model predictions to familiar features, XAI addresses clinician trust, patient engagement, as well as ethical and regulatory requirements. Future research must prioritize prospective, multicenter validation and real-time clinical integration to establish XAI as a cornerstone of precision stone disease management.