Artificial Intelligence in Pediatric Imaging: Current Capabilities and Future Directions

Dear Colleagues,

Artificial intelligence is rapidly reshaping pediatric imaging, building on advances in deep learning, radiomics, and automated volumetric tools to improve diagnosis, risk stratification, and longitudinal brain development assessment in children. The historical arc from handcrafted features to multimodal, data-driven radiogenomic models has created noninvasive links between imaging phenotypes and molecular or genetic signatures. This Special Issue aims to showcase translational AI that advances pediatric neuroimaging and oncologic imaging. It emphasizes explainability and promotes robust clinical validation and governance for child-specific tools. We welcome cutting-edge work on brain age and developmental–trajectory models, radiogenomics, multimodal MRI–genetic integration, foundation or transfer-learning approaches, and explainable AI methods with clinical evaluation. Original research, technical notes, large curated pediatric datasets, clinical validation studies, systematic reviews, and ethics/governance analyses are welcome.

Dr. Mohammad Arafat Hussain
Guest Editor

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