Artificial intelligence-enabled pediatric radiology in low-resource settings: addressing resource constraints in the...

Artificial intelligence (AI) holds immense promise in guiding clinical decision making in pediatric radiology, but its implementation in resource-constrained healthcare systems is limited by several significant challenges. The common AI methods, specifically deep learning models, used for image synthesis, reconstruction and segmentation require high-performance computers (HPC) and large memory capacities, which are often unavailable in low- and middle-income countries, especially in Sub-Saharan Africa. Long reconstruction times, inadequate hardware, and reliance on expensive commercial software further hinder adoption. These issues are compounded by the scarcity of annotated pediatric datasets, variability in imaging protocols, and limited data-sharing infrastructure, all of which widen the AI divide, particularly in pediatric imaging. Even when advanced AI models are developed, deploying them into clinical workflows remains difficult due to poor integration with existing picture archiving and communication systems (PACS) and the limited internet infrastructure for cloud-based solutions and data storage. Addressing these barriers will require intentional efforts to provide affordable high-performance computing resources, open-source pediatric datasets, federated learning approaches, and seamless workflow integration backed by robust region-specific AI regulations. This review sheds light on these barriers and highlights opportunities for AI-enabled solutions to become routine in pediatric radiology on the African continent.