Artificial Intelligence–Assisted MRI in Pediatric Brain Tumors: Clinically Oriented Applications for Diagnosis, Risk Stratification, and Treatment Monitoring

Artificial intelligence (AI) is rapidly transforming the imaging assessment of pediatric brain tumors. This review provides a clinically focused overview of AI-assisted MRI applications for diagnosis, risk stratification, and treatment monitoring in children. For differential diagnosis, deep learning models, particularly for posterior fossa tumors, now demonstrate classification accuracy comparable to expert radiologists using routine sequences, aiding preoperative planning. Automated segmentation tools enable reproducible, RAPNO-compliant volumetry for baseline surgical/radiation planning and standardized longitudinal response assessment, reducing inter-observer variability. Beyond anatomy, emerging radiomics and radiogenomics approaches aim to non-invasively predict molecular alterations, prognosis, and treatment response. However, clinical adoption faces challenges including data heterogeneity, small pediatric datasets, need for multi-center validation, and integration into clinical workflows. Future translation re-quires prospective, pragmatic studies, robust generalizability across age groups and scanners, and careful attention to interpretability and ethical governance. When effectively validated and implemented, AI-powered neuroimaging holds significant promise for enhancing precision and personalizing care in pediatric neuro-oncology.