用于医疗器械的基于机器学习的分类方法的基于风险的评估.
Martin Haimerl1, Christoph Reich2
1Furtwangen University of Applied Sciences, Furtwangen, Germany. Martin.Haimerl@hs-furtwangen.de.
BMC medical informatics and decision making
|March 12, 2025
概括
评估机器学习 (ML) 医疗设备需要基于风险的方法. 目前的研究往往忽略了风险考虑,影响了绩效指标,并可能导致风险增加至196%.
科学领域:
- 医疗器械评估 医疗器械评估
- 机器学习在医疗保健中的应用
- 风险评估方法 风险评估方法
背景情况:
- 机器学习 (ML) 越来越多地用于医疗设备.
- 风险和成本考虑对于评估医疗器械性能至关重要.
- 本研究侧重于基于机器学习的分类模型的基于风险的评估.
研究的目的:
- 评估目前基于机器学习的分类模型中基于风险的指标的使用情况.
- 引入一种将风险和成本整合到ML模型性能指标中的方法.
- 分析风险比率对整体绩效和监管合规性的影响.
主要方法:
- 在ML医疗器械出版物中对基于风险的指标进行文献研究.
- 开发一种风险和成本综合性绩效评估方法.
- 对风险比率影响的分析和与欧盟医疗器械法规的调整.
主要成果:
- 目前大多数出版物都缺乏基于风险的ML模型性能指标.
- 风险考虑对评估结果产生重大影响,潜在风险增加高达196%.
- 根据欧盟法规,基于风险的方法对于ML医疗器械的评估是必要的.
结论:
- 基于风险的方法对于评估基于ML的医疗器械至关重要.
- 当前的科学文献往往忽视了ML模型评估中的风险考虑.
- 拟议的方法与欧盟对医疗器械的监管要求保持一致.
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