Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Assessment of the Landscape and Current Utilization

Purpose of Review: Pediatric urology has been a hub for developing clinically relevant artificial intelligence (AI) models. We provide an overview of existing and novel models, emphasizing the availability and applicability of these technologies to enhance clinical practice. Recent Findings: Current models have demonstrated significant potential in pediatric urology, particularly in vesicoureteral reflux (VUR), hydronephrosis, and lower urinary tract dysfunction. Notable developments include machine learning and deep learning approaches for VUR grading, explainable models that predict obstructive hydronephrosis from ultrasound images alone, and ensemble models that integrate video urodynamic pressure tracings with fluoroscopic images to characterize bladder dysfunction. However, issues with generalizability, transparency, and reproducibility remain prevalent. Standardized reporting guidelines have been proposed to address these concerns and improve model evaluation. Summary: AI integration in pediatric urology is promising for enhancing predictive accuracy and clinical care. While current models show encouraging results, ongoing challenges exist in ensuring that current model development in in silico settings translates into meaningful clinical impact. Future efforts should focus on robust model development, extensive cross-institutional validation, and adherence to standardized reporting guidelines to bridge the gap between AI research and clinical practice, ultimately improving patient outcomes. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.