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人工智能对生物医学数据的准备:Bridge2AI建议

Timothy Clark¹, Harry Caufield², Jillian A Parker³

¹University of Virginia.

bioRxiv : the preprint server for biology

|November 1, 2024

概括

美国国家卫生研究院.

科学领域:

生物医学研究和临床实践.
人工智能 (AI) 和机器学习 (ML) 在医学领域

背景情况:

生物医学研究越来越多地采用AI/ML来获得更深入的见解.
深度学习方法在解释性和伦理方面存在挑战.
美国国家卫生研究院 (NIH) 推出了Bridge2AI,以推进生物医学中的AI/ML.

研究的目的:

提出评估生物医学数据对人工智能准备程度的方法.
概述生物医学中AI/ML的数据标准,标准和考虑因素.
解决道德,法律和社会影响 (ELSI) 和可解释AI (XAI).

主要方法:

开发数据对AI准备的标准.
在Bridge2AI计划中建立数据标准视角.
专注于XAI和ELSI对人工智能技术的考虑.

主要成果:

生物医学AI科学严谨性的基本标准.
评估数据适用于AI/ML分析的方法.
人工智能在医学中的伦理设计和应用指南.

结论:

确定的标准对于严格和道德的生物医学AI至关重要.
桥2AI计划旨在传播数据,工具和方法.
这些标准支持AI/ML在医疗保健中的负责任整合.

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