Performance of adult-trained artificial intelligence models in paediatric imaging—a scoping review
Lene Bjerke Laborie; Jennifer Lee; Edward Antram; Regina Küfner Lein; Susan Cheng Shelmerdine · 2026 · European Radiology
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
OBJECTIVES: This scoping review aims to evaluate the performance of artificial intelligence (AI) models designed for adults when applied to paediatric imaging datasets without additional adaptations, and to quantify performance degradation across different modalities, use-cases and age groups. MATERIALS AND METHODS: A literature search was conducted covering 10 years (1/01/2014-23/06/2025) using terms relating to "child", "adult", "artificial intelligence", "radiology" and "validation/performance". Two reviewers independently extracted data using standardised templates and conducted a narrativ
Abstract by Lene Bjerke Laborie; Jennifer Lee; Edward Antram; Regina Küfner Lein; Susan Cheng Shelmerdine, European Radiology (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1007/s00330-026-12354-5