Artificial intelligence-powered solutions for environmental sustainability in medical imaging: a scoping review.
Authors
Affiliations (15)
Affiliations (15)
- CRRAG Research Group, Division of Radiography, Department of Allied Health, School of Health and Medical Sciences, City St George's, University of London, United Kingdom.
- Magnitiki Tomografia Kerkiras, Corfu, Greece.
- Imaging Informatics Working Group, European Federation of Radiographer Societies, Cumieira, Portugal.
- General Hospital of Attica KAT, Advanced Imaging Modalities Department, Athens, Greece.
- National and Kapodistrian University of Athens, School of Medicine, Athens, Greece.
- University Hospital of Tübingen, Department of Diagnostic and Interventional Radiology, Tübingen, Germany.
- St Savvas Oncological Hospital of Attica, Department of Radiotherapy, Athens, Greece.
- Democritus University of Thrace, School of Medicine, Medical Physics Laboratory, Alexandroupolis, Greece.
- University of Tübingen Faculty of Medicine, Tübingen, Germany.
- Great Ormond Street Hospital for Children NHS Foundation Trust, Department of Clinical Radiology, London, United Kingdom.
- University College London Great Ormond Street Institute of Child Health, London, United Kingdom.
- Imperial College London Faculty of Medicine, London, United Kingdom.
- University Hospital of Tübingen, Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, Tübingen, Germany.
- University Hospital of Tübingen, Institute for Bioinformatics and Medical Informatics (IBMI), Tübingen, Germany.
- European Society of Medical Imaging Informatics, Vienna, Austria.
Abstract
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 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. 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. Although AI-powered solutions hold promise for improving sustainability in medical imaging, the true environmental impact remains largely unexplored. The findings of this work further strengthen the dual role of AI adoption in medical imaging related to environmental sustainability.