ReviewPediatric radiology2026
Leveraging AI solutions for sustainable practice in pediatric radiology: a practical guide and an educational tool.
Review in Pediatric radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Green radiology (part 1): environmental sustainability and energy consumption in medical imaging, with perspectives from Japan.Japanese journal of radiology · 2026Review
- Strategies for radiology faculty recruitment and retention in a competitive market: implications for pediatric radiology.Pediatric radiology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
41733616What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.