Exploring environmental sustainability of artificial intelligence in radiology: A scoping review
AI, Artificial intelligence; CNN, Convolutional neural networks; CT, Computed tomography; CPU, Central Processing Unit; DL, Deep learning; FLOP, Floating-point operation; GHG, Greenhouses gas; GPU, Graphics Processing Unit; LCA, Life Cycle Assessment; LLM, Large Language Model; MeSH, Medical Subject Headings; ML, Machine learning; MRI, Magnetic resonance imaging; NLP, Natural language processing; PUE, Power Usage Effectiveness; TPU, Tensor Processing Unit; USA, United States of America; ViT, Vision Transformer; WUE, Water Usage Effectiveness.
Authors
- Christian Lovis (ORCID: https://orcid.org/0000-0002-2681-8076)
- Mélanie Champendal (ORCID: https://orcid.org/0000-0002-1465-4334)
- Ricardo Teresa Ribeiro
- Jérôme Schmid (ORCID: https://orcid.org/0000-0003-2464-8971)
- Henning Müller (ORCID: https://orcid.org/0000-0001-6800-9878)
- G. Brulé (ORCID: https://orcid.org/0000-0003-4560-8696)
- Belinda Lokaj
- Jamil Zaghir (ORCID: https://orcid.org/0000-0002-8209-6098)
- Valentin Durand de Gevigney
- Polina Boiko
Institutions
- HES-SO University of Applied Sciences and Arts Western Switzerland (CH)
- HES-SO Genève (CH)
- University Hospital of Geneva (CH)
- University Hospitals Geneva Medical Center (US)
Publication Details
- Journal
- European Journal of Radiology
- Published
- 2025-11-20
- DOI
- https://doi.org/10.1016/j.ejrad.2025.112558
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00