Personalized Medicine in Pediatric Urology: From Diagnosis to Individualized Risk Assessment

Pediatric urology has traditionally relied on anatomical classifications, standardized diagnostic pathways, and disease-specific treatment algorithms. However, children with the same anatomical diagnosis may have substantially different risks of disease progression, renal injury, complications, and need for intervention. Personalized and precision medicine aim to integrate clinical, imaging, functional, biological, genomic, and longitudinal information to support individualized risk assessment and decision-making.

This narrative review was based on a targeted literature search of PubMed/MEDLINE, Scopus, Embase and Web of Science databases. Search terms included combinations of "personalized medicine," "precision medicine," "risk stratification," "pediatric urology," "biomarkers," "genomics," and "artificial intelligence," together with terms related to major pediatric urological conditions. Additional relevant publications were identified from reference lists of key articles. The literature was narratively synthesized according to its relevance to individualized risk assessment and clinical decision-making.

Current evidence supports an evolving role for individualized risk assessment in vesicoureteral reflux, antenatal hydronephrosis and ureteropelvic junction obstruction, congenital anomalies of the kidney and urinary tract, hypospadias, undescended testes, and disorders of sex development. Biomarkers, genomic testing, advanced imaging, and artificial intelligence may provide additional information for phenotyping and outcome prediction. However, most predictive and AI-based models remain insufficiently validated, with limitations related to external validation, calibration, reproducibility, clinical utility, and generalizability. Longitudinal reassessment is particularly important because risk may change with growth, disease progression, and treatment response.

Personalized pediatric urology should move beyond diagnosis-based algorithms toward dynamic, risk-adapted decision-making. The goal is not to increase the number of investigations or interventions, but to identify which child is most likely to benefit from additional testing, surveillance, or treatment. Future implementation will depend on robust validation of predictive models and demonstration that personalized approaches improve clinically meaningful outcomes.