Despite the hype surrounding artificial intelligence (AI) in radiology, paediatric imaging has been neglected compared to other sub-specialties such as breast, oncology or neuroimaging.1 This may be partly due to a comparatively larger workload in adult medicine, conveniently providing large training datasets and thereby potentially greater opportunities to automate routine tasks (e.g., cancer screening applications). There are intrinsically challenging aspects surrounding the practice of paediatric radiology, such as the need for a more'hands-on/ human' approach in many cases (e.g., fluoroscopy and ultrasound studies, keeping children calm during examinations), and greater heterogeneity in data due to wide variations of normal findings at different stages of childhood development. Nevertheless, AI could still prove helpful in enhancing children's imaging services, particularly given the current radiology workforce shortages (only 38.5% of institutions in the UK have 24/7 access to a paediatric radiology opinion)2 and national economic hardships – potentially leading to a vicious cycle of fewer job and training opportunities, with even further lack of access to specialist opinion. In this article, we discuss a variety of possible'use cases' in paediatric radiology where AI has either been implemented already or shown early-stage feasibility, while also taking inspiration from the adult literature to propose areas for future development.