在临床人工智能研究时代,探索开放的数据共享和隐私:从重新识别到伪重新识别
Shahin Hallaj1,2, Anna Heinke1,2, Fritz Gerald P Kalaw1,2
1Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA.
EClinicalMedicine
|January 16, 2026
概括
分享临床研究数据加速了医学发现,但也面临着隐私挑战. 新的人工智能 (AI) 方法产生伪重新识别风险,需要用于临床研究的创新数据共享策略.
科学领域:
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
- 生物伦理学生物伦理学
背景情况:
- 在临床研究中开放的数据共享对于推动医学发现至关重要.
- 参与者的隐私和数据保密是数据共享的重大障碍.
- 人工智能 (AI) 的进步对数据去识别和参与者重新识别构成了新的威胁.
研究的目的:
- 讨论人工智能对区分可识别和不可识别的临床数据所带来的挑战.
- 在人工智能驱动的数据分析中引入伪重新识别的概念.
- 探索这些挑战如何在临床研究中为新的开放数据共享实践提供信息.
主要方法:
- 关于人工智能对临床研究数据隐私的影响的概念讨论.
- 介绍了伪重新识别的概念.
- 突出了AI-READI项目中开发的新型开放数据共享方法.
主要成果:
- 人工智能方法模糊了可识别和不可识别数据之间的界限.
- 伪重新识别在临床数据分析中越来越令人担忧.
- 人工智能-READI项目为安全和开放的数据共享提供了一个新模式.
结论:
- 在人工智能时代,重新考虑开放的数据共享实践是必不可少的.
- 需要新的方法来平衡数据可访问性与参与者隐私.
- 人工智能-READI项目展示了一个可行的战略,以推进临床研究中的开放数据共享.
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