Artificial Intelligence in Pediatric Oncology: Toward Personalized Childhood Cancer Care

Abstract

Pediatric oncology has witnessed remarkable advances over the past several decades, resulting in significant improvements in survival for many childhood cancers. Nevertheless, cancer remains one of the leading causes of disease-related mortality among children worldwide, and survivors frequently experience long-term treatment-related complications affecting physical, cognitive, endocrine, cardiovascular, and psychosocial health. The extraordinary biological diversity of childhood malignancies, together with age-dependent physiology, genetic predisposition, developmental considerations, and variable therapeutic responses, necessitates highly individualized approaches to diagnosis and treatment. Recent developments in artificial intelligence (AI), computational oncology, multimodal data integration, and precision medicine have introduced transformative opportunities for personalized childhood cancer care. Artificial intelligence integrates radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, laboratory investigations, electronic health records, physiological monitoring, and longitudinal clinical information into comprehensive computational models capable of supporting diagnosis, molecular classification, prognostic prediction, treatment optimization, toxicity assessment, recurrence monitoring, and survivorship management. Advances in deep learning, transformer architectures, foundation models, graph neural networks, reinforcement learning, and explainable artificial intelligence have further accelerated clinical implementation across pediatric oncology. Despite remarkable progress, challenges remain regarding limited pediatric datasets, data interoperability, algorithmic fairness, ethical governance, cybersecurity, regulatory validation, and equitable healthcare access. This review provides a comprehensive overview of artificial intelligence in pediatric oncology, highlighting computational foundations, current clinical applications, emerging innovations, implementation challenges, and future perspectives toward personalized childhood cancer care.