Artificial intelligence in pediatric nephrology: current applications and emerging frameworks for evidence generation

Artificial intelligence (AI) is rapidly transforming many areas of medicine. Pediatric nephrology may be a field of particular relevance due to its data-rich environments, rarity of diseases, long-term follow-up needs, and persistent evidence gaps. Traditional clinical research methods, including randomized controlled trials, are often difficult to conduct in pediatric nephrology because of small sample sizes, ethical constraints, heterogeneity of disease, and long latency to clinically meaningful outcomes. AI-based methods offer complementary approaches to address these challenges, spanning diagnostic support, prognostic modeling, imaging analysis, treatment personalization, and innovative frameworks for evidence generation. This narrative review provides a focused overview of AI applications that are already available or emerging in pediatric nephrology, integrating lessons from broader nephrology research on real-world data and digital-twin technologies. We refer to established and near-term applications in chronic kidney disease, dialysis, transplantation, imaging, pathology, and genomics in children. We then discuss future directions, including AI-enabled virtual trials and digital twins as potential tools to extend evidence to pediatric populations traditionally excluded from trials. Ethical, regulatory, and equity considerations specific to children are highlighted. We conclude that AI should be viewed as an adjunct, rather than a replacement to conventional clinical research, with particular promise for advancing precision, inclusivity, and timeliness of evidence in pediatric nephrology.