Tadavarthi Yasasvi, Vey Brianna, Krupinski Elizabeth, Prater Adam, Gichoya Judy, Safdar Nabile, Trivedi Hari
Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.).
Radiol Artif Intell. 2020 Nov 11;2(6):e200004. doi: 10.1148/ryai.2020200004. eCollection 2020 Nov.
To provide an overview of important factors to consider when purchasing radiology artificial intelligence (AI) software and current software offerings by type, subspecialty, and modality.
Important factors for consideration when purchasing AI software, including key decision makers, data ownership and privacy, cost structures, performance indicators, and potential return on investment are described. For the market overview, a list of radiology AI companies was aggregated from the Radiological Society of North America and the Society for Imaging Informatics in Medicine conferences (November 2016-June 2019), then narrowed to companies using deep learning for imaging analysis and diagnosis. Software created for image enhancement, reporting, or workflow management was excluded. Software was categorized by task (repetitive, quantitative, explorative, and diagnostic), modality, and subspecialty.
A total of 119 software offerings from 55 companies were identified. There were 46 algorithms that currently have Food and Drug Administration and/or Conformité Européenne approval (as of November 2019). Of the 119 offerings, distribution of software targets was 34 of 70 (49%), 21 of 70 (30%), 14 of 70 (20%), and one of 70 (1%) for diagnostic, quantitative, repetitive, and explorative tasks, respectively. A plurality of companies are focused on nodule detection at chest CT and two-dimensional mammography. There is very little activity in certain subspecialties, including pediatrics and nuclear medicine. A comprehensive table is available on the website .
The radiology AI marketplace is rapidly maturing, with an increase in product offerings. Radiologists and practice administrators should educate themselves on current product offerings and important factors to consider before purchase and implementation.© RSNA, 2020See also the invited commentary by Sala and Ursprung in this issue.
概述购买放射学人工智能(AI)软件时需要考虑的重要因素,以及按类型、亚专业和模态划分的当前软件产品情况。
描述了购买AI软件时需要考虑的重要因素,包括关键决策者、数据所有权和隐私、成本结构、性能指标以及潜在投资回报率。为进行市场概述,从北美放射学会和医学影像信息学会会议(2016年11月至2019年6月)汇总了放射学AI公司名单,然后缩小范围至使用深度学习进行影像分析和诊断的公司。排除了用于图像增强、报告或工作流程管理的软件。软件按任务(重复性、定量性、探索性和诊断性)、模态和亚专业进行分类。
共识别出55家公司的119种软件产品。截至2019年11月,有46种算法目前已获得美国食品药品监督管理局和/或欧洲合格认证。在这119种产品中,软件目标的分布情况分别为:诊断性任务70种中的34种(49%)、定量性任务70种中的21种(30%)、重复性任务70种中的14种(20%)以及探索性任务70种中的1种(1%)。多家公司专注于胸部CT结节检测和二维乳腺摄影。某些亚专业领域(包括儿科和核医学)的活动非常少。网站上提供了一个综合表格。
放射学AI市场正在迅速成熟,产品供应不断增加。放射科医生和医疗机构管理人员在购买和实施之前,应了解当前的产品供应情况以及需要考虑的重要因素。© RSNA,2020另见本期Sala和Ursprung的特邀评论。