Background/Objectives: Artificial intelligence (AI) is transforming neuroimaging by enhancing diagnostic precision and treatment planning. However, its applications in pediatric cancer neuroimaging remain limited. This review assesses the current state, potential applications, and challenges of AI in pediatric neuroimaging for cancer, emphasizing the unique needs of the pediatric population. Methods: A comprehensive literature review was conducted, focusing on AI’s impact on pediatric neuroimaging through accelerated image acquisition, reduced radiation, and improved tumor detection. Key methods include convolutional neural networks for tumor segmentation, radiomics for tumor characterization, and several tools for functional imaging. Challenges such as limited pediatric datasets, developmental variability, ethical concerns, and the need for explainable models were analyzed. Results: AI has shown significant potential to improve imaging quality, reduce scan times, and enhance diagnostic accuracy in pediatric neuroimaging, resulting in improved accuracy in tumor segmentation and outcome prediction for treatment. However, progress is hindered by the scarcity of pediatric datasets, issues with data sharing, and the ethical implications of applying AI in vulnerable populations. Conclusions: To overcome current limitations, future research should focus on building robust pediatric datasets, fostering multi-institutional collaborations for data sharing, and developing interpretable AI models that align with clinical practice and ethical standards. These efforts are essential in harnessing the full potential of AI in pediatric neuroimaging and improving outcomes for children with cancer.

We thank the readers for their thoughtful reading of our review, “Artificial Intelligence for Neuroimaging in Pediatric Cancer”, and for their interest in discussing aspects of segmentation performance metrics

Background/Objectives: Artificial intelligence is transforming neuroimaging by enhancing di-agnostic precision and treatment planning. However, its applications in pediatric cancer neu-roimaging remain limited. This review assesses the current state, potential applications, and challenges of AI in pediatric neuroimaging for cancer, emphasizing the unique needs of the pe-diatric population. Methods: A comprehensive literature review was conducted, focusing on artificial intelligence impact on pediatric neuroimaging through accelerated image acquisition, reduced radiation, and improved tumor detection. Key methods include convolutional neural networks for tumor segmentation, radiomics for tumor characterization, and several tools for functional imaging. We analyzed challenges such as limited pediatric datasets, developmental variability, ethical concerns, and the need for explainable models. Results: Artificial intelligence has shown significant potential to improve imaging quality, reduce scan times, and enhance diagnostic accuracy in pediatric neuroimaging. Artificial intelligence algorithms demonstrated improved accuracy in tumor segmentation and outcome prediction for tumor treatments. Conclusions: Artificial intelligence offers significant potential for enhancing pediatric neuroim-aging in cancer care, aiding in precise diagnoses and personalized treatments. To overcome current limitations, future research should focus on building robust pediatric datasets and developing interpretable models suited for clinical practice.