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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.
Insights
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.
Abstract:
Artificial intelligence (AI) adoption in health care is rapidly accelerating, with pediatrics positioned to benefit from improvements in quality, efficiency, and access to care. Recent frameworks have articulated ethical commitments for trustworthy AI in pediatrics, but less attention has been paid to the foundational infrastructure required to achieve them. In this article, we examine the challenges that emerge at the intersection of AI and pediatrics: unique challenges that AI poses for pediatric care, including nonintuitive errors, impacts on clinician cognition, and new infrastructure requirements; and unique challenges that pediatrics poses for AI, including adequate training data, accommodation of developmental heterogeneity, and navigation of evolving patient autonomy. We then outline the data systems, governance structures, validation frameworks, and public trust infrastructure that must be established before the field can meet these challenges. We conclude with concrete recommendations for clinicians, caregivers, health care systems, government, technologists, and academic institutions seeking to lay the foundation for trustworthy and beneficial AI in pediatrics.
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