Abstract Hemodialysis patients with comorbid urolithiasis represent a high‐risk subgroup within chronic kidney disease, facing unique challenges from altered physiology, including electrolyte imbalances, secondary hyperparathyroidism, and increased stone formation risks. Traditional risk scores, such as the Charlson Comorbidity Index or Recurrence of Kidney Stone (ROKS) nomogram, rely on linear assumptions and fail to capture nonlinear interactions, dialysis‐specific parameters like Kt/V or parathyroid hormone levels, and outcomes like stone recurrence, sepsis, or mortality, leading to suboptimal predictive accuracy. This narrative review explores the potential of machine learning (ML) models, including ensemble methods like Random Forest and XGBoost, and deep learning architectures, which excel in handling high‐dimensional, multimodal data from electronic health records, imaging, and longitudinal trends. Comparative evidence from nephrology and urology domains suggests ML outperforms traditional approaches by 5%–15% in AUC for discrimination, though direct comparisons in hemodialysis‐urolithiasis cohorts are lacking; future studies are needed. However, barriers such as interpretability issues, data biases, and lack of prospective validation persist, necessitating explainable AI tools like SHapley Additive exPlanations (SHAP) and adherence to diversity principles. Future directions emphasize multicenter registries, hybrid models, and decision curve analysis to enhance clinical utility, ultimately shifting toward personalized risk stratification for improved outcomes in this vulnerable population.
Chaulagain et al. (Tue,) studied this question.