为了监督公平性,稳定性和数据集转变的分析系统,健康AIAI转变
Ángel Sánchez-García1, David Fernández-Narro1, Pablo Ferri1
1Biomedical Data Science Lab, Instituto Universitario de Tecnologías de la Información y Comunicaciones (ITACA), Universitat Politècnica de València (UPV), Camino de Vera s/n, Valencia 46022, España.
Studies in health technology and informatics
|October 3, 2025
概括
对人工智能 (AI) 临床决策支持系统 (CDSS) 的持续监测对于患者安全至关重要. ShinAI-Agent提供了一个新的系统,用于对AI性能和公平性的可解释,隐私意识的监控,解决当前的仪表板限制.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 健康 人工智能 伦理学
背景情况:
- 高风险的人工智能 (AI) 系统,如临床决策支持系统 (CDSS),需要持续的性能和公平性评估,以确保患者安全和保护个人权利.
- 当前的健康人工智能监控仪表板面临的挑战是非添加性指标,数据采集 (基础真理标签,敏感变量) 和数据集转移管理,阻碍了跨子组和时间的可靠聚合和分解.
研究的目的:
- 设计和介绍ShinAI-Agent,这是一个模块化系统,用于持续,可解释和对健康AI和CDSS性能和公平性的隐私意识监控.
- 解决现有监测方法的局限性,通过实现跨时间和敏感子组的指标的灵活计算.
主要方法:
- 开发ShinAI-Agent,一个模块化系统,具有探索仪表板,用于性能/公平性指标的时间序列导航,模型校准,决策截止探索和数据集转移监控.
- 实现双层数据库:人工智能结果和案例级数据的代理数据库,以及具有可聚合原始的OLAP架构 (例如基于案例的混矩阵,内置的概率分布).
主要成果:
- 新AI-Agent系统促进了对健康AI和CDSS性能和公平性的持续,可解释和隐私意识的监控.
- 拟议的OLAP架构能够灵活可靠地计算不同时间段和敏感人口子组的绩效和公平性指标.
- 该系统支持遵守伦理和稳定性要求,如欧盟人工智能法案中的要求,并为模型再培训提供建议.
结论:
- ShinAI-Agent提供了一个强大的解决方案,用于对健康AI和CDSS的持续,可解释和隐私意识的监控.
- 该系统的设计解决了指标聚合和数据采集的关键挑战,促进了医疗保健中可靠的AI.
- 这种方法支持监管合规性,并使可靠人工智能的原则变得可操作.
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