ArticleDigital health
Digital innovations for sustainable radiology: Reducing waste, emissions, and inefficiencies in imaging workflows.
Article in Digital health. 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
- Reduced environmental impact in body CT imaging with deep learning reconstruction: experience of a high-volume tertiary referral center.Insights into imaging · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Healthcare systems worldwide face growing pressure to reduce environmental impact while maintaining high-quality, cost-effective care. Radiology, a particularly resource-intensive discipline, contributes significantly to healthcare-related emissions and operational inefficiencies. At the same time, digital technologies-such as artificial intelligence (AI), structured reporting, and smart scheduling-offer powerful tools to address both ecological and economic challenges. For example, AI-driven protocol optimization has been shown to cut energy consumption by up to 56% and examination time by 55%, directly aligning sustainability with operational efficiency. Using radiology as a representative case, this article examines how these innovations can reduce environmental waste, imaging overuse, and workflow inefficiencies, offering a scalable blueprint for healthcare-wide transformation. This commentary draws on a targeted review of recent literature, supplemented by practical insights from clinical radiology practice.
Indexed as
Identifiers
What 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.