法尔斯凯普:生物医学研究的不断发展的人工智能准备框架
Sadnan Al Manir1, Maxwell Adam Levinson1, Justin Niestroy1
1University of Virginia School of Medicine.
bioRxiv : the preprint server for biology
|January 7, 2025
概括
FAIRSCAPE框架为FAIR数据生成可解释AI (XAI) 元数据,确保在研究中道德地部署AI. 这个框架支持数据来源和特征,用于强大的AI模型开发.
科学领域:
- 生物医学信息学 生物医学信息学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 人工智能 (AI) 应用需要可解释性 (XAI) 才能在临床和实验室环境中进行道德和 FAIR (可查找,可访问,可互操作,可重复使用) 的部署.
- 在AI模型培训和应用之前,详细描述数据采集,表征,转换和分发的全面XAI元数据至关重要.
研究的目的:
- 引入FAIRSCAPE框架,用于生成,包装和整合必要的XAI预模型描述元数据.
- 加强人工智能中使用的生物医学数据集的公平性和伦理考虑.
主要方法:
- FAIRSCAPE框架生成深度来源图和数据字典,并对上传的数据,软件和计算进行功能验证.
- 它提供了伦理和语义数据集特征,包括许可和可用性信息.
- 该框架与NIH推的通用库集成,并且符合云计算,在Python 3中实现了服务器和客户端软件,REST API和JavaScript GUI.
主要成果:
- FAIRSCAPE成功地生成和集成关键的模型前XAI元数据,包括来源和数据字典.
- 该框架确保数据集的伦理和语义特征,增强其公平性.
- 它通过命令行,Python函数,REST API和GUI提供灵活的访问.
结论:
- FAIRSCAPE提供了一个强大的解决方案,用于生成和管理XAI元数据,这对于FAIR和生物医学研究中道德AI部署至关重要.
- 该框架的全面功能和灵活的实施方便与现有数据基础设施和存储库无集成.
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