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TRUVACE RECORD VERSION
record: TRV-2026-2316
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-30T18:34:41.657318Z
status: published
lens: g_space
sector: climate
headline: Artificial intelligence-powered solutions for environmental sustainability in medical imaging: a scoping review
dek: Growing evidence shows that medical imaging has a substantial environmental impact. With the increasing adoption of artificial intelligence (AI) in clinical settings, it is important to explore how this technology may support environmental sustainability. Literature searches were performed across eight databases (PubMed, Wiley Online Library, Embase, Web of Science platform, the Cochrane Library, Cumulative Index to Nursing and Allied Health Literature Ultimate, Google Scholar, and MedRxiv) for the period Januar…
gain_title: The reviewed literature proposes that AI in medical imaging could lower energy use by optimizing scheduling, cutting scanner idle time, and reducing CT over-scanning, contrast agent use and radiation dose.
problem_title: (none)
trace_subject: (none)
gain_reading: The reviewed literature proposes that AI in medical imaging could lower energy use by optimizing scheduling, cutting scanner idle time, and reducing CT over-scanning, contrast agent use and radiation dose.
gain_evidence: AI tools in medical imaging could be used to minimize energy consumption by optimizing patient scheduling, decreasing scanner idle times and workstation use
problem_reading: (none)
problem_evidence: (none)
quick_read: A scoping review searched eight databases for work published from January 2020 to June 2026 and included 23 studies on AI and environmental sustainability in medical imaging. Risk of bias was assessed with Joanna Briggs Institute checklists, and results were combined in a narrative synthesis.

The review matters because medical imaging has a substantial environmental footprint and AI is spreading in clinical settings. The authors describe a dual role for AI, and the net environmental effect has not been measured, so the benefits remain largely proposals.
limitation: The source states that the true environmental impact of AI-powered solutions in medical imaging remains largely unexplored, so the proposed sustainability benefits are not demonstrated as net gains.
tag: Evidence-backed gain
key_points: The review included 23 studies drawn from eight databases covering January 2020 to June 2026. | Proposed uses include optimizing patient scheduling and decreasing scanner idle times and workstation use. | Other proposed uses include reducing CT over-scanning, contrast agent usage and radiation dose. | Low-field MRI and curtailing low-value imaging were also suggested as ways to save energy. | The authors say AI may also harm the environment in ways not yet quantified.
rundown: The authors searched PubMed, Wiley Online Library, Embase, Web of Science, the Cochrane Library, CINAHL Ultimate, Google Scholar and MedRxiv. Only full-text English sources were eligible, and bias was assessed with Joanna Briggs Institute checklists.

From 23 eligible studies, the literature proposed several ways AI could reduce energy and resource use in imaging, including better scheduling, less idle scanner time, fewer unnecessary scans, and lower contrast agent and radiation dose.

The paper notes that AI technologies can also affect the environment harmfully. It concludes that the true environmental impact is largely unexplored, reinforcing the dual role of AI adoption.
sources:
- peer_reviewed | Diagnostic and Interventional Radiology | https://doi.org/10.4274/dir.2026.264308 | 2026-10-30
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