BACKGROUND: Paediatric chest imaging is central to diagnosing respiratory and cardiopulmonary disease, particularly in low- and middle-income countries (LMICs) where pneumonia remains a leading cause of childhood mortality and radiology expertise is scarce. Artificial intelligence (AI) could expand access, standardise quality and support task-shifting in these "diagnostic deserts," yet most systems are trained and validated on adult datasets from high-income settings, and paediatric radiographs form only a small minority of major public training cohorts - raising concerns about safety, generalisability and equity when such models are deployed in children. OBJECTIVE: To synthesise current applications of AI across paediatric chest radiography, lung ultrasound, computed tomography and MRI, with emphasis on LMIC workflows, and to define what is required for safe, paediatric-specific deployment. MATERIALS AND METHODS: Narrative/pictorial review of the published literature, complemented by the authors' real-world evaluation of a generalist vision-language model (MedGemma) on adult and paediatric chest radiographs, illustrated with representative clinical cases. RESULTS: Beyond diagnosis, AI shows potential in quality assurance, lung-ultrasound guidance and multilingual reporting. Real-world experience from CAD4Kids and from MedGemma's evaluation - including critical failures in detecting pericardial effusion and tuberculosis without explicit clinical context - illustrates common failure modes and the ethical implications of domain shift. Key challenges include infrastructure constraints, dataset scarcity and the need for age-aware, explainable models. CONCLUSION: Rather than adapting adult systems, future tools must be designed from inception for children and the environments in which they live, prioritising federated learning, multimodal integration, robust validation across age strata and multilingual communication with caregivers.
Muringathuparambil et al. (Wed,) studied this question.