Filice Ross W, Mongan John, Kohli Marc D
Chief of Imaging Informatics, MedStar Georgetown University Hospital, Washington, DC.
Associate Chair of Translational Informatics and Director of the Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging and Center for Intelligent Imaging, University of California San Francisco, San Francisco, California.
J Am Coll Radiol. 2020 Nov;17(11):1405-1409. doi: 10.1016/j.jacr.2020.09.045. Epub 2020 Oct 6.
Many radiologists are considering investments in artificial intelligence (AI) to improve the quality of care for our patients. This article outlines considerations for the purchasing process beginning with performance evaluation. Practices should decide whether there is a need to independently verify performance or accept vendor-provided data. Successful implementations will consider who will receive AI results, how results will be presented, and the impact on efficiency. The article provides education on infrastructure considerations including the benefits and drawbacks of best-of-breed and platform approaches in addition to highly specialized server requirements like graphical processing unit availability. Finally, the article presents financial and quality and safety considerations, some of which are unique to AI. Examples include whether additional revenue could be obtained, as in the case of mammography, and whether an AI model unintentionally leads to reinforcing healthcare disparities.
许多放射科医生正在考虑投资人工智能(AI),以提高为患者提供的护理质量。本文概述了从性能评估开始的采购过程中的注意事项。医疗机构应决定是否需要独立验证性能,还是接受供应商提供的数据。成功的实施将考虑谁将接收AI结果、结果将如何呈现以及对效率的影响。本文还介绍了基础设施方面的注意事项,包括最佳-of-breed和平台方法的优缺点,以及图形处理单元可用性等高度专业化的服务器要求。最后,本文介绍了财务、质量和安全方面的注意事项,其中一些是AI所特有的。例如,是否可以像乳腺摄影那样获得额外收入,以及AI模型是否会无意中加剧医疗保健差距。