放射学中的环境可持续性和人工智能:双刃剑
Florence X Doo1, Jan Vosshenrich1, Tessa S Cook1
1From 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.).
放射学领域的人工智能 (AI) 面临着双重挑战:它有助于温室气体排放,同时也为环境可持续性提供解决方案. 需要制定战略来减少人工智能.
科学领域:
- 医疗保健中的环境可持续性
- 医学成像技术 医学成像技术
- 人工智能应用程序 人工智能应用程序
背景情况:
- 气候变化对健康构成重大风险,需要在医疗保健中减少排放.
- 医学成像有助于温室气体 (GHG) 排放,特别是通过数据中心和人工智能.
- 随着大数据和人工智能在放射学领域的日益广泛使用,能源消耗也在大幅增加.
研究的目的:
- 检查人工智能 (AI) 在放射学中的双重作用,涉及环境可持续性.
- 讨论在放射学中减轻人工智能相关温室气体排放的策略.
- 探索AI如何提高医学成像中的可持续性.
主要方法:
- 关于人工智能,温室气体排放和放射学可持续性的当前文献的审查.
- 对影响医疗成像中的能源消耗和效率的人工智能应用的分析.
- 探索减少人工智能相关排放和利用人工智能实现可持续发展的战略.
主要成果:
- 放射学中的AI应用增加了能源需求和温室气体排放.
- 人工智能还可以通过更快的MRI扫描,高效的调度和减少低价值成像来提高可持续性的潜力.
- 现有策略可以减少人工智能相关的排放,并增强放射学的环境足迹.
结论:
- 解决放射学中人工智能的环境影响对于全球健康至关重要.
- 为了实现可持续性,利用人工智能可以带来成本降低和更好的患者治疗结果等共同利益.
- 需要进一步的研究来解决人工智能的环境影响和可持续应用方面的知识差距.
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