在预防性健康数据-AI研究中的负责任监督方面
Sam H A Muller1,2, Johannes J M van Delden1, Ghislaine J M W van Thiel1
1Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.
PLOS digital health
|December 29, 2025
概括
卫生研究中的人工智能 (AI) 增强了精准医学,但也增加了监视,带来了伦理挑战. 负责任的治理,包括透明度和公共监督,对于可信的健康数据-AI实践至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 生物伦理学生物伦理学
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 在健康数据研究中的整合为精准医学和慢性疾病管理提供了潜力.
- 然而,健康人工智能也大大提升了监控,引发了大量的伦理和社会问题.
- 卫生监测,特别是关于数据-人工智能研究和创新,仍然是一个研究不足的领域.
研究的目的:
- 提供对健康数据-人工智能监视的概念分析.
- 通过人工智能在医疗保健中检查监控实践的演变和扩大.
- 评估健康数据-人工智能监控的影响和挑战,并提出负责任的治理策略.
主要方法:
- 对健康数据-AI监视的概念分析.
- 使用Hypermarker研究项目的案例研究.
- 追踪医学,公共卫生和数字卫生技术监控的历史演变.
- 分析AI扩大现有监控实践及其影响.
主要成果:
- 卫生数据-AI监控带来了诸如普遍性,超集,功能爬行,超可视性,分析和监督组合的形成等影响.
- 超标记者项目实施了保护措施和措施,以应对这些挑战.
- 确定的主要挑战包括加强可信度 (公平,公平),确保问责制 (透明度),促进公共控制和监督.
结论:
- 在健康数据-人工智能研究中推进负责任的治理至关重要.
- 建议包括实施社区咨询小组,独立审查委员会,数据-AI司法框架,透明度仪表板和开放的监督周期.
- 加强可信度,问责制和公共控制对于道德健康数据-AI监控至关重要.
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