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TRV-2026-1042Certified recordPeer-reviewed

Pediatric Artificial Intelligence in Radiology: Unmet Needs and Next Steps-<i>AJR</i> Expert Panel Review

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…

Health · P Space — documented harm · certified 2026-09-10 · v1 · article view · machine-readable

Current reading — problem

Pediatric medical imaging is substantially underrepresented in AI development and validation, with scarce datasets and off-label use of adult models risking bias and patient safety.

What this doesn’t fix

Pediatric AI development is constrained by limited data availability and validation, including rare diseases and fragmented institutional data.

Evidence

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Truvace Impact Record TRV-2026-1042, v1: “Pediatric Artificial Intelligence in Radiology: Unmet Needs and Next Steps-<i>AJR</i> Expert Panel Review.” Truvace, 2026-09-10. /record/TRV-2026-1042 (accessed at citation time). sha256 3717c64a53635aa3…

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