在放射学中探索人工智能的环境可持续性:一个范围审查
Mélanie Champendal1, Belinda Lokaj2, Valentin Durand de Gevigney3
1HESAV School of Health Sciences. Vaud, HES-SO University of Applied Sciences and Arts Western, Switzerland; Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
European journal of radiology
|November 23, 2025
概括
放射学中人工智能 (AI) 的环境可持续性研究有限,但正在增长. 本综述综合了当前关于人工智能的知识.
科学领域:
- 放射学和医学成像学 医学成像学
- 环境科学 环境科学
- 计算机科学和人工智能 人工智能
背景情况:
- 人工智能 (AI) 越来越多地融入到放射学实践中.
- 人工智能在放射学中的环境影响尚未得到充分理解.
- 越来越需要评估和减轻AI在医疗保健中的环境影响.
研究的目的:
- 综合现有的关于AI在放射学中的环境可持续性的文献.
- 确定用于评估AI在医学成像中的环境足迹的关键指标.
- 突出在放射学中减轻人工智能的环境影响的策略.
主要方法:
- 根据乔安娜·布里格斯研究所的方法进行了范围审查.
- 在主要数据库 (MEDLINE,Embase,CINAHL,Web of Science) 进行了搜索,搜索了2014年至2024年期间的英语和法语出版物.
- 包括专注于人工智能,环境可持续性和医学成像的研究,并应用了特定的排除标准.
主要成果:
- 13项研究符合纳入标准,揭示了四个主要主题:能源消耗,碳足迹,计算资源和水消耗.
- 报告的关键指标包括二氧化碳相当的排放量,培训时间,电力使用效率 (PUE) 和用水量.
- 提出的可持续性战略包括轻量级模型架构,模型优化技术 (量化,修剪),高效优化器和早期停止.
结论:
- 关于放射学中可持续人工智能的研究机构目前很少,但正在迅速扩大.
- 本综述提供了必要的指标和策略,以促进更透明和对环境负责任的放射学人工智能开发.
- 未来的研究和实践应该优先考虑医疗成像中的AI的一致和可衡量的环境影响评估.
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