From Static Diagnosis to Dynamic Guidance : Evolution of Artificial Intelligence in Pediatric Neuroimaging

The pediatric brain represents a dynamic biological target characterized by rapid myelination and functional reorganization, which presents unique challenges for conventional, adult-centric artificial intelligence (AI) models. This review provides a structured overview of the evolution of AI applications in pediatric neuroimaging and neurosurgery, tracing the transition from early standardized pipelines and handcrafted imaging biomarkers to contemporary deep learning-based approaches for segmentation, prediction, and anomaly detection. Recent advances indicate a paradigm shift from static image interpretation toward dynamic and interactive intelligence, in which AI systems actively support clinical decision-making during surgery rather than functioning solely as diagnostic tools. This new paradigm is supported by four technological domains : brain foundation models designed to capture age-aware neurodevelopmental representations; spatial computing technologies for three-dimensional, context-aware-visualization; physical AI systems integrating robotic safety constraints; and multimodal AI agents that act as cognitive surgical copilots by synthesizing imaging, physiological, and intraoperative data in real time. By shifting the role of AI from preoperative assessment to intraoperative guidance, this paradigm offers new opportunities to enhance surgical precision, safety, and workflow efficiency in pediatric neurosurgery. This review aims to provide neurosurgeons with a conceptual framework for understanding and adopting next-generation AI technologies that align with the dynamic nature of the developing brain and the clinical demands of pediatric neurosurgical care.