From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, GRB 297, Boston, MA 02155 (D.D., T.A., B.C.B., K.D., J.A.B., K.J.D.); Department of Radiology, Duke University, Durham, NC (W.F.W., C.J.R.); Department of Radiology, Stanford University, Stanford, Calif (M.P.L., D.B.L., C.P.L.); Radiology Partners, El Segundo, Calif (N.K.); and Department of Radiology, Grandview Medical Center, Birmingham, Ala (B.A.).
Radiology. 2022 Dec;305(3):555-563. doi: 10.1148/radiol.212151. Epub 2022 Aug 2.
As the role of artificial intelligence (AI) in clinical practice evolves, governance structures oversee the implementation, maintenance, and monitoring of clinical AI algorithms to enhance quality, manage resources, and ensure patient safety. In this article, a framework is established for the infrastructure required for clinical AI implementation and presents a road map for governance. The road map answers four key questions: Who decides which tools to implement? What factors should be considered when assessing an application for implementation? How should applications be implemented in clinical practice? Finally, how should tools be monitored and maintained after clinical implementation? Among the many challenges for the implementation of AI in clinical practice, devising flexible governance structures that can quickly adapt to a changing environment will be essential to ensure quality patient care and practice improvement objectives.
随着人工智能（AI）在临床实践中的作用不断发展，治理结构负责监督临床 AI 算法的实施、维护和监测，以提高质量、管理资源并确保患者安全。本文为临床 AI 实施所需的基础设施建立了一个框架，并提出了治理路线图。路线图回答了四个关键问题：谁决定实施哪些工具？评估应用程序实施时应考虑哪些因素？如何在临床实践中实施应用程序？最后，工具在临床实施后应如何进行监测和维护？在临床实践中实施 AI 面临的诸多挑战中，设计能够快速适应不断变化的环境的灵活治理结构对于确保患者护理质量和实践改进目标至关重要。
