医疗保健中的机器学习操作:一个范围审查
Anjali Rajagopal1, Shant Ayanian2, Alexander J Ryu2
1Department of Medicine, Artificial Intelligence and Innovation, Mayo Clinic Rochester, MN.
Mayo Clinic proceedings. Digital health
|April 10, 2025
概括
医疗保健中的机器学习操作 (MLOps) 正在出现. 本综述综合了MLOps最佳实践的文献,并确定了临床采用的知识差距.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 临床操作 临床操作
背景情况:
- 机器学习 (ML) 工具在医疗保健中越来越多地使用.
- 支持机器学习部署的操作流程,称为机器学习操作 (MLOps),仍在发展.
研究的目的:
- 综合现有关于医疗保健MLOps的文献.
- 识别知识差距,并为临床实践采用提供见解.
主要方法:
- 进行了全面的范围审查.
- 在MEDLINE,PubMed,谷歌学者,Embase和Scopus中进行了搜索.
- 在两阶段的选过程中,包括了148篇相关文章.
主要成果:
- 在医疗保健MLOps中确定了七个关键主题领域:模型监测,自动再培训,伦理/公平/偏见,临床工作流集成,基础设施/人力资源/技术堆,监管和财务考虑.
- 该审查概述了最佳实践,并突出了这些领域的知识差距.
- 评估了当前文献的优点和弱点.
结论:
- 本综述为医疗保健ML从业者和消费者提供了有价值的概述.
- 它确定了在临床环境中开发和实施MLOps的关键领域.
- 需要进一步的研究,以解决成功采用MLOps的识别知识差距.
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