Leveraging AI solutions for sustainable practice in pediatric radiology: a practical guide and an educational tool.

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Abstract

Pediatric imaging presents distinct and urgent sustainability challenges, in part driven by its unique subspecialty demands: safeguarding the lifetime radiation risks of children, providing accurate diagnoses during their dynamic periods of growth, and ensuring family-centered care. These unique challenges impose additional strains on our ecosystem. To help alleviate this added burden, we propose a three-pillar model of sustainability specific to pediatric imaging, encompassing environmental, economic, and social factors. In particular, we address the sustainability challenges central to pediatric radiology by introducing AI not only as a tool for diagnostic accuracy, but also as an engine for sustainable practice. In this review, we move beyond the generic discussions of "green" radiology by illustrating how AI can be deployed to confront specific challenges across all three pillars of sustainability. Our review is centered around nine concrete, clinically grounded AI solutions, with three examples dedicated to each pillar. When strategically applied, these AI solutions have the potential to optimize energy efficiency, decrease consumables, extend equipment lifecycles, streamline operations, increase revenue, enhance transparency, improve pediatric care, promote equity, and empower patients and families. We also address other critical considerations in this sustainability domain, including AI's own carbon footprint and the need for pediatric-specific validation. Collectively, AI's extensive capabilities can drive our pediatric imaging towards diagnostic excellence, while optimizing environmental health, operational efficiency, and social equity.

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