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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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生物医学数据库需要在每一步都对人工智能/机器学习应用程序进行治理.

Ellen Wright Clayton1, Susannah Rose2, Camille Nebecker3

  • 1Center for Biomedical Ethics and Society, Vanderbilt University Medical Center, Nashville, TN 37203, United States.

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概括

美国国家卫生研究院的Bridge2AI计划创建了AI准备好的生物医学数据集,面临数据收集和治理方面的挑战. 学到的经验教训强调了对未来人工智能计划的负责任的数据使用和道德考虑.

关键词:
数据访问数据的访问.数据隐私 隐私数据 隐私数据治理 治理 治理 治理 治理 治理获得知情同意的情况.

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科学领域:

  • 生物医学数据科学是生物医学数据科学.
  • 医疗保健中的人工智能
  • 数据治理数据治理

背景情况:

  • 国家卫生研究院 (NIH) 启动了Bridge2AI计划,以促进人工智能在生物医学研究中的采用.
  • 资助了四个旗舰数据集,设计用于人工智能 (AI) 和机器学习 (ML) 技术.
  • 确保负责任的数据收集和治理对于人工智能在健康中的伦理应用至关重要.

研究的目的:

  • 讨论收集和管理AI准备的生物医学数据集所面临的挑战和经验教训.
  • 概述创建和使用这些数据集的步骤和伦理考虑.
  • 突出在人工智能驱动的研究中负责任的数据管理策略.

主要方法:

  • 数据选择标准和理由.
  • 纳入公众关注和参与者的同意.
  • 建立数据存储,访问,共享和下载的协议.
  • 在整个数据生命周期中解决道德,法律,社会和实际挑战.

主要成果:

  • 识别了人工智能准备数据集创建中的伦理,法律,社会和实际挑战.
  • 突出了公共输入,数据存储和访问控制中的各种项目特定选择.
  • 证明了解决未来数据存储和使用问题的重要性.

结论:

  • 尽管Bridge2AI数据集治理流程各不相同,但它们具有共同的元素.
  • 这些共同元素为未来以人工智能为重点的数据程序提供了有价值的策略.
  • 经验教训强调了需要强大的数据治理框架,以确保在生物医学研究中负责任地实施人工智能.