From the University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD (F.X.D.); Department of Radiology, University Hospital Basel, Basel, Switzerland (J.V., T.H.); Department of Radiology, New York University, New York, NY (J.V., L.M.); Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pa (T.S.C.); Joint Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada (E.P.R.P.A., K.H.); Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (S.A.W.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (J.W.G.); Toronto General Hospital Research Institute, University Health Network, University of Toronto, 585 University Ave, 1 PMB-298, Toronto, ON, Cananda M5G 2N2 (K.H.); and Department of Medical Imaging, University Medical Imaging Toronto, University of Toronto, Toronto, Ontario, Canada (K.H.).
Radiology. 2024 Feb;310(2):e232030. doi: 10.1148/radiol.232030.
According to the World Health Organization, climate change is the single biggest health threat facing humanity. The global health care system, including medical imaging, must manage the health effects of climate change while at the same time addressing the large amount of greenhouse gas (GHG) emissions generated in the delivery of care. Data centers and computational efforts are increasingly large contributors to GHG emissions in radiology. This is due to the explosive increase in big data and artificial intelligence (AI) applications that have resulted in large energy requirements for developing and deploying AI models. However, AI also has the potential to improve environmental sustainability in medical imaging. For example, use of AI can shorten MRI scan times with accelerated acquisition times, improve the scheduling efficiency of scanners, and optimize the use of decision-support tools to reduce low-value imaging. The purpose of this in Focus article is to discuss this duality at the intersection of environmental sustainability and AI in radiology. Further discussed are strategies and opportunities to decrease AI-related emissions and to leverage AI to improve sustainability in radiology, with a focus on health equity. Co-benefits of these strategies are explored, including lower cost and improved patient outcomes. Finally, knowledge gaps and areas for future research are highlighted.
根据世界卫生组织的数据，气候变化是人类面临的最大单一健康威胁。包括医学影像在内的全球医疗保健系统必须应对气候变化对健康的影响，同时还要解决在提供医疗服务过程中产生的大量温室气体 (GHG) 排放。数据中心和计算工作越来越成为放射科温室气体排放的主要贡献者。这是因为大数据和人工智能 (AI) 应用的爆炸式增长导致开发和部署 AI 模型需要大量的能源。然而，人工智能也有可能改善医学影像的环境可持续性。例如，人工智能的使用可以缩短 MRI 扫描时间，加快采集速度，提高扫描仪的调度效率，并优化决策支持工具的使用，以减少低价值成像。本期聚焦文章的目的是讨论放射科环境可持续性和人工智能交叉点上的这种双重性。进一步讨论的是减少与人工智能相关的排放的策略和机会，并利用人工智能来提高放射科的可持续性，重点是健康公平。探讨了这些策略的共同效益，包括降低成本和改善患者预后。最后，强调了知识差距和未来研究领域。
