This narrative review maps the current landscape of artificial intelligence (AI) in paediatric and fetal neuroradiology, critically evaluating current practice, barriers to clinical adoption, and future potential. We searched for peer-reviewed studies from the last decade, focusing on image segmentation, lesion detection, classification, prognostication, and clinical decision support in paediatric brain imaging. Particular consideration was given to unique paediatric factors such as brain development and data scarcity. AI techniques, notably deep learning, have demonstrated success in automated brain tumour segmentation, detection of epileptogenic lesions, and radiomics-based classifiers predicting tumour histology and molecular subtypes. Despite these advancements, clinical adoption remains limited. Key barriers identified include high implementation costs, limited large-scale diverse paediatric datasets, and concerns regarding safety, bias, and regulatory approval. Addressing these issues through data-sharing initiatives, federated learning, paediatric-specific validation, and revised ethical and regulatory frameworks is crucial. Ongoing multi-institutional collaborations can facilitate AI's integration into paediatric neuroradiology, complementing radiologists and improving paediatric care.
Kelly et al. (2026) studied this question.