Parenting Journal · Volume 2 · Issue 5 · Licensed source edition
Current challenges for global equity related to the implementation of artificial intelligence in pediatric imaging
Originally published 2026-07-23 · Pediatric Radiology
Springer Science and Business Media LLC · DOI 10.1007/s00247-026-06717-9 ↗
AI / system type
Narrow imaging AI/ML; consensus-based equity framework
Family implicationsDesign, ethics, stakeholder or technical evidence with child/family implications; household benefits are not assumed.
The AI connection
The paper addresses development, validation, and deployment of pediatric imaging AI.
Why this reading matters
Translates broad fairness language into practical questions families can raise with hospitals and vendors.
Editorial reading guide · Respect
Could this AI imaging service work fairly for families outside well-resourced hospitals?
The paper links fairness to infrastructure, language, costs, data representation, and sustainable support. Its roadmap makes equitable care a set of institutional responsibilities rather than a promise that a model alone will expand access.
Study context
World Federation of Pediatric Imaging representatives combined experience, consensus discussion, and targeted literature/document searches across seven implementation domains.
Age / stage: Children receiving imaging, especially in low- and middle-income settings
Read with three questions
- Which barriers arise before a scan reaches an algorithm?
- How do localization and long-term costs affect equitable access?
- Who is responsible for testing subgroup performance and explaining children’s data use?
Keep the limits in view
The targeted review and coauthor discussion did not include systematic evidence-quality assessment or empirical evaluation of the roadmap. Recommendations require local adaptation and do not establish that proposed implementation strategies reduce disparities.
The text below is the original article’s licensed repository edition. Its wording is not rewritten. Use the Paper Agent beside the text to locate evidence; open the source for figures, tables and complete context.
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Follow the original.
Original authorship, publication date, journal and DOI are preserved. This issue’s TRICK placement and reading questions are editorial additions, not claims made by the original authors.
Rutger A. J. Nievelstein; Amit Gupta; Joanna Kasznia-Brown; Nasreen Mahomed; Jaishree Naidoo; Marla B. K. Sammer (2026). Current challenges for global equity related to the implementation of artificial intelligence in pediatric imaging Pediatric Radiology. https://doi.org/10.1007/s00247-026-06717-9
Permissions checked against the saved article notice; repository publication notices screened 2026-11-04. This is not a new peer-review decision. The source authors and publishers are not represented as endorsing this issue.