The Digital Revolution in Pediatric Radiology: From Ultrasound to AI-Driven Diagnostics – A Narrative Review

Background: The rapid development in AI and imaging technologies has enabled pediatric radiology to become digital. The objective of modern diagnostic methods is to achieve balance between radiation safety and accuracy in diagnosis by adapting imaging techniques to the anatomical, physiological, and ethical requirements of pediatric patients. Aim: The aim of this study is to summarize recent advancements in artificial intelligence applications across pediatric imaging modalities and to evaluate their impact on diagnostic accuracy, workflow efficiency, and patient safety. Methods: Recent peer-reviewed studies in pediatric radiology and artificial intelligence were analyzed to conduct a narrative review. The primary focus included advancements in ultrasound, MRI, CT, and hybrid imaging systems, as well as AI-driven diagnostic applications, workflow optimization, and ethical considerations. Results: Ultrasound remains the preferred choice, and quantitative tools like contrast-enhanced ultrasonography and elastography render it more valuable. Diffusion imaging, spectroscopy, and faster acquisition methods have made magnetic resonance imaging more accessible and less probable to require sedation. Thanks to advances in CT and PET/CT, often enhanced by iterative and AI-driven reconstruction, radiation exposure has declined considerably. Hybrid PET/MRI provides both molecular and structural details while minimizing radiation exposure significantly. Artificial intelligence is now an integral part of almost all subspecialties in pediatrics. Conclusions: Integrating AI, radiomics, and hybrid modalities is altering theranostic processes in medicine and shows how crucial it is to monitor ethics, equitable access, and professional learning possibilities. To ensure worldwide equity, it's necessary to establish clear validation processes, collaborate across fields, and launch international initiatives.