Leiner Tim, Bennink Edwin, Mol Christian P, Kuijf Hugo J, Veldhuis Wouter B
Department of Radiology | E.01.132, Utrecht University Medical Center, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands.
Image Sciences Institute, Utrecht University Medical Center, Utrecht, The Netherlands.
Insights Imaging. 2021 Feb 2;12(1):11. doi: 10.1186/s13244-020-00931-1.
AI provides tremendous opportunities for improving patient care, but at present there is little evidence of real-world uptake. An important barrier is the lack of well-designed, vendor-neutral and future-proof infrastructures for deployment. Because current AI algorithms are very narrow in scope, it is expected that a typical hospital will deploy many algorithms concurrently. Managing stand-alone point solutions for all of these algorithms will be unmanageable. A solution to this problem is a dedicated platform for deployment of AI. Here we describe a blueprint for such a platform and the high-level design and implementation considerations of such a system that can be used clinically as well as for research and development. Close collaboration between radiologists, data scientists, software developers and experts in hospital IT as well as involvement of patients is crucial in order to successfully bring AI to the clinic.
人工智能为改善患者护理提供了巨大机遇，但目前几乎没有实际应用的证据。一个重要障碍是缺乏设计良好、供应商中立且面向未来的部署基础设施。由于当前的人工智能算法范围非常狭窄，预计一家典型医院将同时部署多种算法。管理所有这些算法的独立单点解决方案将难以应对。解决此问题的办法是建立一个专门的人工智能部署平台。在此，我们描述这样一个平台的蓝图以及该系统的高层设计和实施考量，该系统可用于临床以及研发。放射科医生、数据科学家、软件开发人员和医院信息技术专家之间的密切合作以及患者的参与对于成功将人工智能引入临床至关重要。
