Pierce Jonathan D, Rosipko Beverly, Youngblood Lisa, Gilkeson Robert C, Gupta Amit, Bittencourt Leonardo Kayat
Department of Radiology, University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, Cleveland, Ohio.
Director, Radiology Informatics, University Hospitals Cleveland Medical Center, Cleveland, Ohio.
J Am Coll Radiol. 2021 Nov;18(11):1497-1505. doi: 10.1016/j.jacr.2021.08.023. Epub 2021 Sep 28.
Although interest in artificial intelligence (AI) has exploded in recent years and led to the development of numerous commercial and noncommercial algorithms, the process of implementing such tools into day-to-day clinical practice is rarely described in the burgeoning AI literature. In this report, we describe our experience with the successful integration of an AI-enabled mobile x-ray scanner with an FDA-approved algorithm for detecting pneumothoraces into an end-to-end solution capable of extracting, delivering, and prioritizing positive studies within our thoracic radiology clinical workflow. We also detail several sample cases from our AI algorithm and associated PACS workflow in action to highlight key insights from our experience. We hope this report can help inform other radiology enterprises seeking to evaluate and implement AI-related workflow solutions into daily clinical practice.
尽管近年来对人工智能（AI）的兴趣激增，并催生了众多商业和非商业算法，但在蓬勃发展的人工智能文献中，很少描述将此类工具应用于日常临床实践的过程。在本报告中，我们描述了将一款配备人工智能的移动X光扫描仪与美国食品药品监督管理局（FDA）批准的用于检测气胸的算法成功集成到一个端到端解决方案中的经验，该解决方案能够在我们的胸部放射学临床工作流程中提取、传递并优先处理阳性研究结果。我们还详细介绍了来自我们的人工智能算法和相关图像存档与通信系统（PACS）工作流程的几个实例，以突出我们经验中的关键见解。我们希望本报告能为其他寻求评估并将人工智能相关工作流程解决方案应用于日常临床实践的放射学企业提供参考。
