Evidence map›Paper›PMID 42429898›Full record

ReviewJapanese journal of radiology2026

Green radiology (part 1): environmental sustainability and energy consumption in medical imaging, with perspectives from Japan.

Hideki Ota, Tsuneo Yamashiro, Kanako Kunishima Kumamaru, Soma Kumasaka, Aki Kido, Kumi Ozaki, Yumiko Kono, Minako Azuma, Tetsuya Fukuda, Noriko Aida and 1 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Japanese journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Hideki OtaDepartment of Diagnostic Radiology, Tohoku University Hospital, 1-1 Seiyo-machi, Aoba-ku, Sendai, 9808524, Japan. hideki.ota.d6@tohoku.ac.jp.ORCID http://orcid.org/0000-0002-7239-3152
Tsuneo YamashiroDepartment of Radiology, St. Luke's International Hospital, Tokyo, Japan.ORCID http://orcid.org/0000-0002-1904-2548
Kanako Kunishima KumamaruFaculty of Health Data Science , Juntendo University, Chiba, Japan.ORCID http://orcid.org/0000-0001-5924-3283
Soma KumasakaDepartment of Applied Medical Imaging, Gunma University Graduate School of Medicine, Maebashi, Japan.ORCID http://orcid.org/0000-0001-5541-3847
Aki KidoDepartment of Radiology , Toyama University Hospital, Toyama, Japan.ORCID http://orcid.org/0000-0001-5131-2870
Kumi OzakiDepartment of Radiology, Hamamatsu University School of Medicine, Hamamatsu, Japan.ORCID http://orcid.org/0000-0002-1454-7512
Yumiko KonoDepartment of Radiology , Kansai Medical University, Hirakata, Japan.ORCID http://orcid.org/0000-0002-7977-0272
Minako AzumaFaculty of Medicine, Department of radiology, University of Miyazaki, Miyazaki, Japan.ORCID http://orcid.org/0000-0002-4837-4197
Tetsuya FukudaDepartment of Radiology, National Cerebral and Cardiovascular Center, Suita, Japan.ORCID http://orcid.org/0000-0001-7579-0674
Noriko AidaDepartment of Diagnostic Radiology, Yokohama City University Graduate School of Medicine, Yokohama, Japan.ORCID http://orcid.org/0000-0002-2776-2115
Sustainable Radiology Committee of the Japan Radiological Society

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Climate change is an important public health challenge, and healthcare itself contributes to greenhouse gas emissions. Within healthcare, radiology is energy intensive because modern imaging services require high-power equipment, cooling systems, digital infrastructure, consumables, and continuous operational readiness. This review summarizes current evidence on the environmental footprint of radiology and discusses practical strategies for implementing Green Radiology, with particular attention to Japan. Life cycle assessment studies indicate that the operational phase of imaging systems, especially electricity use, is an important contributor to radiology-related emissions, while non-productive idle periods represent a key opportunity for mitigation. MRI, CT, interventional radiology, picture archiving and communication systems, workstations, and emerging artificial intelligence (AI)-related infrastructure each contribute to this footprint in different ways. Practical measures include equipment power management, workflow optimization, protocol optimization, energy-aware information technology management, careful implementation of AI, and reduction of low-value imaging, many of which may also improve operational efficiency and reduce costs. However, these strategies must be implemented with attention to emergency readiness, scanner warm-up requirements, diagnostic accuracy, patient safety, and clinical workflow. In Japan, high CT and MRI scanner density with relatively low per-scanner utilization may increase non-productive energy use, but broad equipment distribution also supports accessibility, regional equity, and emergency preparedness. At the system level, reducing the environmental footprint of radiology in Japan therefore requires balanced regional coordination, shared resource utilization, selective consolidation where appropriate, and effective allocation of limited radiologist resources. Green Radiology should aim to reduce avoidable environmental burden while maintaining diagnostic accuracy, patient safety, equitable access to medical imaging, and high-quality patient care.

Indexed as

Artificial intelligenceCarbon footprintEnergy consumptionEnvironmental sustainabilityGreen RadiologyLife cycle assessmentLow-value imagingMedical imagingPlanetary healthSustainable Radiology

Identifiers

PMID42429898

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.