医疗保健中的机器学习:与隐私,可解释性和偏见相关的伦理考虑
Thomas Hofweber1, Rebecca L Walker2
1Department of Philosophy, University of North Carolina at Chapel Hill.
North Carolina medical journal
|October 28, 2024
概括
这项调查探讨了机器学习在医学中的伦理挑战,重点关注训练数据,可解释性和偏见. 它检查了对患者隐私,自主和健康公平的影响.
科学领域:
- 医疗信息学医学信息学
- 医疗保健中的人工智能
背景情况:
- 机器学习 (ML) 模型为推进医疗应用提供了巨大的潜力.
- 然而,将ML整合到医疗保健中引发了复杂的伦理考虑.
研究的目的:
- 为了调查与医学中的机器学习相关的伦理挑战.
- 专注于包括培训数据,模型解释性和算法偏差在内的关键领域.
主要方法:
- 文献综述和现有关于医疗保健中的ML伦理研究的综合.
- 分析与数据隐私,患者自主权和健康公平相关的伦理问题.
主要成果:
- 培训数据实践可以延续或加剧现有的健康差异.
- 缺乏模型解释性阻碍了对临床决策的信任和问责制.
- 在ML模型中的算法偏差对患者安全和公平护理构成风险.
结论:
- 解决伦理挑战对于在医学中负责任地开发和部署ML至关重要.
- 确保ML系统中的公平,透明和隐私对于公平的健康结果至关重要.
- 需要进一步的研究和监管框架来指导在医疗保健中的伦理ML实施.
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