The use of artificial intelligence to predict postoperative outcomes following percutaneous nephrolithotomy: A systematic review of prognostic modeling studies.

Artificial intelligence (AI) is increasingly applied in clinical practice to enhance prediction of postoperative outcomes. This systematic review evaluated the performance and clinical relevance of AI-based prognostic models for patients undergoing percutaneous nephrolithotomy (PCNL). A comprehensive search of PubMed, Embase, Scopus, Web of Science, and Google Scholar was conducted on 7 August 2025 to identify original studies that used AI to predict outcomes such as stone-free status. Risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool-Artificial Intelligence extension. A narrative synthesis of study characteristics, and reported performance metrics of AI models was conducted. Twenty-one studies involving 24,087 patients met the inclusion criteria. Tree-based, support vector, discriminant, and regression models achieved the highest median AUCs (0.79-0.81) for predicting stone-free status, whereas neural networks showed lower performance (median 0.60). Tree-based and similarity-based models performed best for predicting the need for adjuvant therapy. Neural networks performed well for bleeding-related outcomes (median AUC 0.87), while tree-based and regression models showed more consistent performance for procedural complications and hospitalization. Infection-related outcomes were predicted most accurately by tree-based and neural network models (median AUCs 0.89-0.90). Temporal trends revealed a shift from early reliance on neural networks and support vector machines to increased use of tree-based approaches in recent years. Overall, AI models offer useful support for perioperative decision-making, though their reliability varies across outcomes. Model performance is highest for stone-free status and infection-related outcomes, but lower for rare or poorly-defined endpoints such as bleeding or peri-procedural complications, reflecting limitations in available features and outcome heterogeneity. Clinically, current models may inform management but are not yet sufficient to dictate care. Future research should focus on standardized outcome definitions, multi-institutional datasets, and integration of high-dimensional data such as imaging and radiomics to develop robust, externally validated predictive tools for prospective implementation in PCNL practice.