Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology.
Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology.
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在放射学临床实践中实施机器学习算法的策略。
DOI：
10.1148/radiol.223170
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发表时间：
2024
期刊：
影响因子：
19.7
通讯作者：
Gee,JamesC
中科院分区：
文献类型：
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作者：
Chae,Allison;Yao,MichaelS;Sagreiya,Hersh;Goldberg,AriD;Chatterjee,Neil;MacLean,MatthewT;Duda,Jeffrey;Elahi,Ameena;Borthakur,Arijitt;Ritchie,MarylynD;Rader,Daniel;Kahn,CharlesE;Witschey,WalterR;Gee,JamesC
尽管最近机器学习（ML）在医疗保健领域的应用取得了进展， 在美国，临床医学几乎没有什么好处和改进， 医院设置。为了促进ML方法的临床适应， 审查提出了一个逐步实施的标准框架， 人工智能进入放射学的临床实践， 三个关键组成部分：问题识别、利益相关者协调，以及 管道集成最近的文献和经验证据综述 在放射成像应用中证明了这种方法的合理性，并提供了一种 组织实施工作以帮助其他医院的探讨 实践利用ML来改善患者护理。临床试验注册号04242667© RSNA，2024
Despite recent advancements in machine learning (ML) applications in health care, there have been few benefits and improvements to clinical medicine in the hospital setting. To facilitate clinical adaptation of methods in ML, this review proposes a standardized framework for the step-by-step implementation of artificial intelligence into the clinical practice of radiology that focuses on three key components: problem identification, stakeholder alignment, and pipeline integration. A review of the recent literature and empirical evidence in radiologic imaging applications justifies this approach and offers a discussion on structuring implementation efforts to help other hospital practices leverage ML to improve patient care.Clinical trial registration no. 04242667© RSNA, 2024