Senior Director, Data Science Office, Mass General Brigham, Boston, Massachusetts; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; Data Science Office, Mass General Brigham, Boston, Massachusetts.
Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts; Data Science Office, Mass General Brigham, Boston, Massachusetts.
J Am Coll Radiol. 2023 Mar;20(3):352-360. doi: 10.1016/j.jacr.2023.01.002.
The multitude of artificial intelligence (AI)-based solutions, vendors, and platforms poses a challenging proposition to an already complex clinical radiology practice. Apart from assessing and ensuring acceptable local performance and workflow fit to improve imaging services, AI tools require multiple stakeholders, including clinical, technical, and financial, who collaborate to move potential deployable applications to full clinical deployment in a structured and efficient manner. Postdeployment monitoring and surveillance of such tools require an infrastructure that ensures proper and safe use. Herein, the authors describe their experience and framework for implementing and supporting the use of AI applications in radiology workflow.
众多基于人工智能 (AI) 的解决方案、供应商和平台给本就复杂的临床放射科实践带来了挑战。除了评估和确保可接受的本地性能和工作流程适应性以改善成像服务外，AI 工具还需要包括临床、技术和财务在内的多个利益相关者协作，以有条理且高效的方式将潜在可部署的应用程序推向全面临床部署。此类工具的部署后监测和监控需要确保正确和安全使用的基础设施。在此，作者描述了他们在放射科工作流程中实施和支持 AI 应用的经验和框架。
