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Building the Foundations for Trustworthy AI in Pediatrics
Samuel G Finlayson1,2,3, Aaron Wightman1,2,4,5, Elliott Mark Weiss1,2,4,6
1Seattle Children's Hospital, Seattle, Washington.
Pediatrics
|July 20, 2026
Summary
Foundational infrastructure is crucial for trustworthy artificial intelligence (AI) in pediatrics. Addressing unique challenges in pediatric AI requires robust data systems, governance, and validation for improved care quality and access.
Area of Science:
- Pediatric healthcare
- Medical artificial intelligence (AI)
- Health informatics
Background:
- Artificial intelligence (AI) adoption is accelerating in healthcare, particularly in pediatrics.
- Ethical frameworks for trustworthy AI in pediatrics exist, but foundational infrastructure needs more attention.
Purpose of the Study:
- Examine challenges at the intersection of AI and pediatrics.
- Outline necessary infrastructure for trustworthy AI in pediatric care.
Main Methods:
- Analysis of unique challenges posed by AI in pediatrics (e.g., errors, cognition, infrastructure).
- Analysis of unique challenges posed by pediatrics for AI (e.g., data, development, autonomy).
- Outline of essential data systems, governance, validation, and public trust infrastructure.
Main Results:
- AI presents unique challenges in pediatrics, including nonintuitive errors and infrastructure needs.
- Pediatrics presents unique challenges for AI, such as data adequacy and developmental heterogeneity.
- Establishing data systems, governance, validation, and public trust is essential for beneficial AI.
Conclusions:
- Concrete recommendations are provided for stakeholders to build trustworthy AI in pediatrics.
- Proactive development of infrastructure is key to realizing AI's benefits in pediatric care.