Artificial intelligence-powered solutions for environmental sustainability in medical imaging: a scoping review
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Nikolaos Stogiannos1,2,3, Kleanthis Konstantinidis4,5, Stephan Ursprung6
1CRRAG Research Group, Division of Radiography, Department of Allied Health, School of Health and Medical Sciences, City St George's, University of London, United Kingdom.
Purpose:
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.
Methods:
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 January 2020-June 2026. Full-text sources in the English language were eligible for inclusion. The Joanna Briggs Institute checklists were employed for risk of bias assessment. All data were analyzed using a textual narrative synthesis.
Results:
In total, 23 studies were deemed eligible for inclusion. The literature proposed that AI tools in medical imaging could be used to minimize energy consumption by optimizing patient scheduling, decreasing scanner idle times and workstation use, reducing computed tomography over-scanning, contrast agent usage, and radiation dose, harnessing low-field magnetic resonance imaging, and curtailing low-value imaging. However, AI technologies may also impact the environment in harmful ways.
Conclusion:
Although AI-powered solutions hold promise for improving sustainability in medical imaging, the true environmental impact remains largely unexplored.
Clinical Significance:
The findings of this work further strengthen the dual role of AI adoption in medical imaging related to environmental sustainability.
