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医学人工智能在放射科的应用:由谁决定以及如何决定?

Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?

机构信息

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.).

出版信息

Radiology. 2022 Dec;305(3):555-563. doi: 10.1148/radiol.212151. Epub 2022 Aug 2.


DOI:10.1148/radiol.212151
PMID:35916673
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9713445/
Abstract

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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6760/9713445/e1432a2db682/radiol.212151.VA.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6760/9713445/e1432a2db682/radiol.212151.VA.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6760/9713445/e1432a2db682/radiol.212151.VA.jpg

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本文引用的文献

[1]
2020 ACR Data Science Institute Artificial Intelligence Survey.

J Am Coll Radiol. 2021-8

[2]
Artificial intelligence in radiology: 100 commercially available products and their scientific evidence.

Eur Radiol. 2021-6

[3]
Trustworthiness of Artificial Intelligence Models in Radiology and the Role of Explainability.

J Am Coll Radiol. 2021-8

[4]
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BMJ Health Care Inform. 2021-2

[5]
How to Implement AI in the Clinical Enterprise: Opportunities and Lessons Learned.

J Am Coll Radiol. 2020-11

[6]
Workflow Applications of Artificial Intelligence in Radiology and an Overview of Available Tools.

J Am Coll Radiol. 2020-11

[7]
Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension.

BMJ. 2020-9-9

[8]
The myth of generalisability in clinical research and machine learning in health care.

Lancet Digit Health. 2020-8-24

[9]
A Clinician's Guide to Artificial Intelligence: How to Critically Appraise Machine Learning Studies.

Transl Vis Sci Technol. 2020-2-12

[10]
Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering Group.

Nat Med. 2020-6

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