Parenting Journal · Volume 2 · Issue 5 · Licensed source edition
A systematic review of data sources for artificial intelligence applications in pediatric brain tumors in Europe: implications for bias and generalizability
Originally published 2023-10-27 · Frontiers in Oncology
Frontiers Media SA · DOI 10.3389/fonc.2023.1285775 ↗
AI / system type
Mixed narrow predictive/imaging AI; dataset evidence review
Family implicationsDesign, ethics, stakeholder or technical evidence with child/family implications; household benefits are not assumed.
The AI connection
The review examines datasets behind AI diagnosis, prognosis, and treatment-support tools for pediatric brain tumors.
Why this reading matters
Connects data representation and privacy governance to families facing rare, heterogeneous childhood cancers.
Editorial reading guide · Respect
Were children like mine represented in the data behind this cancer AI tool?
Training cohorts were often small and geographically concentrated, and many otherwise relevant papers omitted patient ages. These patterns explain why a tool developed elsewhere may not fairly represent an individual child’s age or condition.
Study context
The review selected forty-five European-affiliated AI studies from searches through May 2022; twenty-five used exclusively pediatric cohorts.
Age / stage: Pediatric brain-tumor cohorts; twenty included studies also used adult data
Read with three questions
- How did cohort size and geography constrain generalizability?
- Why does missing age information matter for pediatric AI?
- What privacy protections would make broader data collaboration acceptable?
Keep the limits in view
English-language and European-affiliation restrictions narrow the evidence base. The review assessed data-driven sources of bias rather than testing algorithmic discrimination or clinical outcomes, and inaccessible industrial datasets may be missing.
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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.
Alberto Eugenio Tozzi; Ileana Croci; Paul Voicu; Francesco Dotta; Giovanna Stefania Colafati; Andrea Carai; Francesco Fabozzi; Giuseppe Lacanna; Roberto Premuselli; Angela Mastronuzzi (2023). A systematic review of data sources for artificial intelligence applications in pediatric brain tumors in Europe: implications for bias and generalizability Frontiers in Oncology. https://doi.org/10.3389/fonc.2023.1285775
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.