医疗人工智能的综合风险分析框架:一种混合方法方法.
Yali Wang1, Zihao Deng1, Zhaohua Deng1
1School of Management, Huazhong University of Science and Technology, Wuhan, China.
Risk analysis : an official publication of the Society for Risk Analysis
|February 20, 2026
概括
患者将数据质量和隐私作为医疗人工智能 (AI) 中的主要风险优先考虑. 了解这些AI风险对于安全的医疗保健转型至关重要.
科学领域:
- 医疗人工智能 医疗人工智能
- 医疗风险分析 医疗风险分析
- 患者安全 患者安全
背景情况:
- 人工智能 (AI) 正在改变医疗保健,在医疗AI决策中引入新的挑战.
- 需要一个强大的风险分析框架来有效应对这些挑战.
研究的目的:
- 为医疗人工智能开发一个全面的风险分析框架.
- 识别和优先考虑患者对医疗AI风险的担忧.
主要方法:
- 混合方法方法:在新闻文章上模拟潜在的迪里克莱特分配 (LDA) 主题,对患者采访进行理论分析,对患者调查进行基于选择的联合 (CBC) 分析.
- 对1618篇新闻文章和21名患者的半结构面试进行分析,确定了关键的风险属性.
- 在396名患者的在线调查中,评估了6个已识别的风险属性的偏好.
主要成果:
- 确定了八个初始风险属性,再精细化为六个患者优先考虑的问题:数据质量,隐私和安全,社会偏见,系统性能,责任归因和算法推断.
- 患者风险优先考虑:数据质量 (30.20%),隐私和安全 (29.50%),社会偏见 (19.10%),系统性能 (13.70%),责任归因 (6.87%) 和算法推断 (0.59%).
结论:
- 提出了一个新的医疗AI风险分析框架.
- 研究结果为医疗政策制定者,人工智能开发者和风险分析师提供了实用的见解,以提高患者的安全性和对医疗人工智能的信任.
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