AI Integration in the Clinical Workflow
AI Integration in the Clinical Workflow
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DOI：
10.1007/s10278-021-00525-3
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发表时间：
2021-10-22
影响因子：
4.4
通讯作者：
Korfiatis, Pangiotis
中科院分区：
文献类型：
--
作者：
Blezek, Daniel J.;Olson-Williams, Lonny;Korfiatis, Pangiotis
机器学习和人工智能（AI）算法在应用于医学成像时，对于解决重要的临床需求具有重要的前景;然而，将算法集成到放射科具有挑战性。自动化算法被成功地集成到工作流程中，但通常是封闭的系统，无法供现场研究人员部署算法。人工智能研究人员不需要创建一次性的解决方案，而是需要一个通用的多用途集成系统。在这里，我们为一个旨在将AI算法快速部署到放射科医生工作流程中的系统提供了一组用例和要求。该系统使用符合标准的医学结构化报告（DICOM SR）数字成像和通信，在临床环境中向放射科医生呈现AI测量结果，结果和发现，并允许接受或拒绝结果。该系统还为后处理技术人员实现反馈机制，以根据放射科医生的指示校正结果。我们展示了一个身体组成的算法和一个用于确定多囊肾患者的肾脏总体积的算法的集成。
Machine learning and artificial intelligence (AI) algorithms hold significant promise for addressing important clinical needs when applied to medical imaging; however, integration of algorithms into a radiology department is challenging. Vended algorithms are integrated into the workflow, successfully, but are typically closed systems and unavailable for site researchers to deploy algorithms. Rather than AI researchers creating one-off solutions, a general, multi-purpose integration system is desired. Here, we present a set of use cases and requirements for a system designed to enable rapid deployment of AI algorithms into the radiologist's workflow. The system uses standards-compliant digital imaging and communications in medicine structured reporting (DICOM SR) to present AI measurements, results, and findings to the radiologist in a clinical context and enables acceptance or rejection of results. The system also implements a feedback mechanism for post-processing technologists to correct results as directed by the radiologist. We demonstrate integration of a body composition algorithm and an algorithm for determining total kidney volume for patients with polycystic kidney disease.