Artificial Intelligence in Rare Pediatric Solid Tumor Research and Clinical Care: A Scoping Review.

BACKGROUND: Clinical and research advances for children with solid tumors are limited by their rare nature. Artificial intelligence (AI) and machine learning (ML) hold potential for advancing diagnosis, risk stratification, and treatment in rare diseases where individual patients contribute high-dimensional data. This review characterizes current AI/ML applications in rare pediatric solid tumors. METHODS: PubMed, Embase, and Web-of-Science Core Collection databases were queried for relevant articles published before February 10, 2025. Eligible studies applied AI/ML methods to study rare tumors in pediatric populations (< 19 years). Articles were evaluated for tumor diagnosis, AI/ML methods, clinical applications, comparators, effectiveness, and interpretability. RESULTS: Twenty-three studies (2009-2025) were included. Hepatoblastoma (12/23) and pediatric thyroid cancers (4/23) were most frequently studied. Supervised learning predominated (20/23), followed by unsupervised (9/23), deep learning (4/23), and hybrid/ensemble models (6/23). Applications included diagnosis (12/23), prognosis (10/23), and risk stratification (9/23). Twenty studies reported effectiveness measures, with many models achieving AUCs > 0.85. In comparative analysis (17/23), AI/ML often equaled or exceeded expert consensus, traditional models, or alternative algorithms. Eight studies reported external validation. CONCLUSIONS: Current AI/ML research in rare pediatric extra-cranial solid tumors focuses on diagnosis, risk stratification, and prognosis, often outperforming traditional methods. Future work should prioritize external validation and clinical applicability.