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在生物医学研究中对人工智能的质量控制采样方法
S F Chetverikov1, K M Arzamasov2, A E Andreichenko3
1Head of the Sector of the Development of Systems for the Implementation of Intelligent Medical Technologies; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health care Department, 24/1 Petrovka St., Moscow, 127051, Russia.
Sovremennye tekhnologii v meditsine
|June 30, 2023
概括
评估人工智能 (AI) 采样方法用于生物医学实践中的质量控制,显示了80项研究的最佳样本大小. 这确保了代表性,并平衡了AI在医疗保健中的风险.
科学领域:
- 生物医学信息学 生物医学信息学
- 医疗保健中的人工智能
- 统计质量控制 统计质量控制
背景情况:
- 人工智能 (AI) 结果的定期质量控制在生物医学实践中至关重要.
- 现有各种采样方法,每种采样方法都有不同的统计依据和适用性.
- 人工智能在医疗保健中的整合需要强大的方法来验证人工智能生成的数据.
研究的目的:
- 评估不同采样方法的有效性,以便定期对生物医学环境中的AI结果进行质量控制.
- 在医疗保健系统内确定AI驱动的医学图像分析的最佳采样策略.
主要方法:
- 采样方法的分析,包括点统计估计,假设测试,统计表和GOST R ISO 2859-1-2007的分析.
- 考虑从1000到10万个医学研究的人口的样本大小.
- 在莫斯科医疗保健系统内使用人工智能进行医学图像分析的实验.
主要成果:
- 当只有I型错误是关键时,点统计估计是合适的.
- 统计假设测试允许考虑I型和II型错误.
- 确定了80项研究的最佳样本大小,用于医疗图像分析中的AI质量控制,平衡代表性,风险和成本.
结论:
- 80项研究的最佳样本大小确保了AI在医学图像分析中的代表性和高效的质量控制.
- 选择采样方法取决于具体的统计参数和所需的容错率.
- 有效的抽样策略对于AI在医疗保健中的可靠实施至关重要.
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