Peer-reviewed article
Artificial intelligence in pediatric nephrology: current applications and emerging frameworks for evidence generation.
Giovanni Pellegrino, Giovanni F M Strippoli ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This is a narrative review of artificial intelligence applications in pediatric nephrology, focusing on diagnostic support, prognostic modeling, and imaging analysis. The authors discuss how AI might address research challenges in rare kidney diseases in children, including small sample sizes and long follow-up periods. The review covers current and emerging AI uses in chronic kidney disease, dialysis, transplantation, and genomics, while highlighting ethical and equity considerations. The authors propose AI as a complement to, rather than replacement for, conventional clinical research methods.
Key points
- Pediatric nephrology is data-rich but faces research challenges due to small sample sizes, ethical constraints, and long latency to outcomes
- AI applications span diagnostic support, prognostic modeling, imaging analysis, treatment personalization, and evidence generation frameworks
- AI-enabled virtual trials and digital twins are proposed as tools to extend evidence to pediatric populations traditionally excluded from trials
- Ethical, regulatory, and equity considerations specific to children must be addressed in AI development
- AI should be viewed as an adjunct to conventional clinical research rather than a replacement
Limitations
This is a narrative review that synthesizes existing literature and frameworks rather than presenting original research or empirical evidence of AI interventions. It does not evaluate the effectiveness of any specific AI application in improving child health outcomes.
Abstract
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.