Chatterjee Neil, Duda Jeffrey, Gee James, Elahi Ameena, Martin Kristen, Doan Van, Liu Hannah, Maclean Matthew, Rader Daniel, Borthakur Arijitt, Kahn Charles, Sagreiya Hersh, Witschey Walter
Department of Radiology, University of Pennsylvania, Philadelphia, USA.
Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, USA.
J Imaging Inform Med. 2025 Feb;38(1):368-379. doi: 10.1007/s10278-024-01200-z. Epub 2024 Jul 31.
Although numerous AI algorithms have been published, the relatively small number of algorithms used clinically is partly due to the difficulty of implementing AI seamlessly into the clinical workflow for radiologists and for their healthcare enterprise. The authors developed an AI orchestrator to facilitate the deployment and use of AI tools in a large multi-site university healthcare system and used it to conduct opportunistic screening for hepatic steatosis. During the 60-day study period, 991 abdominal CTs were processed at multiple different physical locations with an average turnaround time of 2.8 min. Quality control images and AI results were fully integrated into the existing clinical workflow. All input into and output from the server was in standardized data formats. The authors describe the methodology in detail; this framework can be adapted to integrate any clinical AI algorithm.
尽管已经发表了众多人工智能算法，但临床上使用的算法数量相对较少，部分原因在于难以将人工智能无缝集成到放射科医生及其医疗企业的临床工作流程中。作者开发了一种人工智能编排器，以促进人工智能工具在大型多地点大学医疗系统中的部署和使用，并使用它对肝脂肪变性进行机会性筛查。在为期60天的研究期间，在多个不同物理位置处理了991例腹部CT，平均周转时间为2.8分钟。质量控制图像和人工智能结果被完全整合到现有的临床工作流程中。服务器的所有输入和输出均采用标准化数据格式。作者详细描述了该方法；这个框架可以进行调整，以集成任何临床人工智能算法。
