Enzmann Dieter R, Arnold Corey W, Zaragoza Edward, Siegel Eliot, Pfeffer Michael A
Chair, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California.
Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California.
J Am Coll Radiol. 2020 Oct;17(10):1299-1306. doi: 10.1016/j.jacr.2020.03.032. Epub 2020 May 5.
Diagnostic radiology (DxR), having had successful serial co-evolutions with imaging equipment and PACS, is faced with another. With a backdrop termed "globotics transition," it should create an IT and informatics infrastructure capable of integrating artificial intelligence (AI) into current critical communication functions of PACS and incorporating functions currently residing in balkanized products. DxR will face the challenge of adopting sustaining and disruptive AI innovations simultaneously. In this co-evolution, a major selection force for AI will be increasing the flow of information and patients; "increasing" means faster flow over larger areas defined by geography and content. Larger content includes a broader spectrum of imaging and nonimaging information streams that facilitate medical decision making. Evolution to faster flow will gravitate toward a hierarchical IT architecture consisting of many small channels feeding into fewer larger channels, something potentially difficult for current PACS. Smartphone-like architecture optimized for communication and integration could provide a large-channel backbone and many smaller feeding channels for basic functions, as well as those needing to innovate rapidly. New, more flexible architectures stimulate market competition in which DxR could act as an artificial selection force to influence development of faster increased flow in current PACS companies, in disruptors such as consolidated AI companies, or in entirely new entrants like Apple or Google. In this co-evolution, DxR should be able to stimulate design of a modern communication medium that increases the flow of information and decreases the time and energy necessary to absorb it, thereby creating even more indispensable clinical value for itself.
诊断放射学(DxR)与成像设备和PACS成功地进行了一系列协同发展,如今又面临新的挑战。在“全球自动化转型”这一背景下,它应创建一个信息技术和信息学基础设施,能够将人工智能(AI)集成到PACS当前的关键通信功能中,并纳入目前分散在不同产品中的功能。DxR将面临同时采用持续性和颠覆性AI创新的挑战。在这种协同发展中,AI的一个主要选择力量将是增加信息和患者的流量;“增加”意味着在由地理和内容定义的更大区域内实现更快的流量。更大的内容包括更广泛的成像和非成像信息流,有助于医疗决策。向更快流量的演进将趋向于一种分层的信息技术架构,由许多小通道汇入较少的大通道,这对当前的PACS来说可能具有潜在困难。针对通信和集成进行优化的类似智能手机的架构可以提供一个大通道主干以及许多用于基本功能以及那些需要快速创新的功能的较小汇入通道。新的、更灵活的架构会刺激市场竞争,在这种竞争中,DxR可以作为一种人工选择力量,影响当前PACS公司、诸如整合后的AI公司等颠覆者或像苹果或谷歌这样的全新进入者中更快增加流量的发展。在这种协同发展中,DxR应该能够刺激设计一种现代通信媒介,增加信息流并减少吸收信息所需的时间和精力,从而为自身创造更多不可或缺的临床价值。