Pediatric Artificial Intelligence in Radiology: Unmet Needs and Next Steps-AJR Expert Panel Review.
TL;DR
The panel gives key recommendations, emphasizing the importance of an implementation roadmap to establish a dedicated pediatric AI infrastructure and standards that are essential to ensure diagnostic accuracy, workflow efficiency, and optimal clinical outcomes for children while minimizing bias and protecting patient safety.
Abstract
Artificial intelligence (AI) applications have transformed radiology, yet pediatric medical imaging remains substantially underrepresented in AI development, validation, regulation, and implementation. Unlike adults, children go through continuous physiologic and anatomic changes that require age-specific models trained on representative developmental data. However, pediatric AI is limited by scarce publicly available datasets, fragmented institutional data, rare diseases, heterogeneous reporting practices, and insufficient external validation. Ethical and regulatory challenges are also a concern in children, including consent for secondary data use, off-label use of adult-trained AI models, and the need for postdeployment surveillance. Additionally, reimbursement is misaligned and must be optimized to allow innovation. This AJR Expert Panel Narrative Review examines the current landscape of pediatric AI in radiology and proposes practical priorities to support its safe and equitable adoption. The panel gives key recommendations, emphasizing the importance of an implementation roadmap to establish a dedicated pediatric AI infrastructure and standards that are essential to ensure diagnostic accuracy, workflow efficiency, and optimal clinical outcomes for children while minimizing bias and protecting patient safety.