实验室医学中机器学习解决方案的性能指标
Nicholas C Spies1, David P Ng2
1Department of Pathology, University of Utah, Salt Lake City, UT, United States.
Laboratory medicine
|June 13, 2025
概括
在实验室医学中实施机器学习需要严格的性能评估. 本综述详细介绍了这些AI解决方案临床应用的方法,最佳实践和陷.
科学领域:
- 实验室医学 实验室医学
- 人工智能的人工智能是人工智能.
- 临床诊断 临床诊断 临床诊断
背景情况:
- 实验室医学的机器学习 (ML) 解决方案在研究中普遍存在,但在临床实践中很少.
- 巨大的投资和整合挑战阻碍了在日常医疗保健中采用ML工具.
- 强大的性能评估对于将ML从概念转变为临床实用性至关重要.
研究的目的:
- 审查在临床实验室环境中评估基于ML的解决方案的常用方法.
- 概述ML工具性能全面评估的最佳实践.
- 在评估过程中识别潜在的挑战和陷.
主要方法:
- 在实验室医学中对ML的评估策略的文献综述.
- 分析常见的指标和验证技术.
- 讨论临床实施的实际考虑.
主要成果:
- 实验室医学中的ML评估框架多样化,但往往缺乏标准化.
- 关键绩效指标 (KPI) 和验证方法对于证明临床实用性至关重要.
- 陷包括数据偏差,概括性问题和不充分的现实世界测试.
结论:
- 标准化,全面的评估对于成功地将ML纳入实验室医学中的临床整合至关重要.
- 应对实施挑战需要仔细考虑绩效指标和潜在的陷.
- 有效的评估协议将加速在诊断中采用可靠的ML工具.
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