记录AI用于健康成像项目的临床和成像数据的非识别过程
Haridimos Kondylakis1, Rocio Catalan2, Sara Martinez Alabart3
1FORTH-ICS, Heraklion, Crete, Greece. kondylak@ics.forth.gr.
Insights into imaging
|May 30, 2024
概括
医学成像中的人工智能 (AI) 需要大型数据集进行开发. 本文详细介绍了欧盟项目中的非识别方法,以确保AI在医疗保健中的隐私遵守.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 数据 隐私 数据 隐私 数据
背景情况:
- 人工智能 (AI) 正在将医学成像转变为积极主动的医疗保健.
- 人工智能开发需要大量,多样化和标准化的现实世界数据集.
- 患者数据隐私至关重要,需要在使用前进行强有力的非识别.
研究的目的:
- 在五个专注于癌症成像数据的欧盟项目中探索非识别方法.
- 介绍创建符合伦理和GDPR规定的欧洲医疗成像平台的方法.
- 在未来的项目中提供洞察力和指导方针,用于处理数据去标识.
主要方法:
- 在五个欧盟 (EU) 项目中分析了非识别策略.
- 专注于与癌症有关的医学成像数据.
- 检查遇到的问题和实施的解决方案.
主要成果:
- 介绍了每个欧盟项目的个人去标识方法.
- 描述了成像数据去识别的具体挑战和成功解决方案.
- 学到的经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验经验.
结论:
- 有效的非识别对于利用AI在医学成像中至关重要.
- 提出的欧盟项目为人工智能医疗保健中的数据隐私提供了有价值的指导方针.
- 标准化的非识别过程是建立可靠的AI用于健康成像的关键.
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