AI integration in pediatric radiology: perspectives from international academic leaders

Artificial intelligence (AI) is increasingly integrated into radiology, but pediatric imaging remains underrepresented in implementation studies. To assess the current status, barriers, and enablers of AI adoption in Pediatric Radiology from a global leadership perspective.

A cross-sectional international survey of department leaders and division chiefs from pediatric radiology centers worldwide was conducted. The questionnaire included 14 items across domains of AI deployment, enablers, barriers, ethical concerns, stakeholder involvement, and future directions. Descriptive statistics were used for analysis.

Eighteen institutions completed the survey (69% response rate). Sixteen centers (88.9%) reported implementing at least one AI tool, with bone age assessment remaining the most widely used application (44.4%). Other applications included image segmentation and quantification (22.2%), imaging protocol optimization (16.7%), and natural language processing (16.7%). The mean clinical impact rating was 3.56/5, with only 16.7% describing AI as "transformational." The most frequently cited barriers were lack of pediatric-specific datasets (83.3%), integration challenges (66.7%), high cost or unclear return on investment (ROI) (50%), and cybersecurity concerns (44.4%). Enablers were primarily human rather than technical, with vendor maturity and integration (72.2%) and internal champions (66.7%) most frequently highlighted. Regional variation was observed, with NLP/reporting clustered in North America and transformational impact ratings concentrated in the Asia-Pacific centers. A slim majority (55.6%) agreed that pediatric AI research currently overemphasizes model development over clinical integration, ethics, and sustainability.

Global pediatric radiology leaders report cautious but growing AI adoption. Addressing pediatric data scarcity, enhancing multicenter collaboration, and prioritizing clinical integration and sustainability will be essential for safe and effective deployment.

Question How are leading pediatric radiology centers worldwide implementing AI, and what institutional barriers and enablers determine successful clinical integration? Findings Most centers deploy at least one AI tool, but perceived impact remains modest due to pediatric data scarcity and workflow integration challenges. Clinical relevance Addressing pediatric-specific dataset limitations through multicenter collaboration, empowering institutional AI champions, and prioritizing clinical integration over model development alone will be essential for ensuring AI benefits children as effectively as adults.