机器学习,医疗保健资源分配和患者同意
1Centre for Technomoral Futures, University of Edinburgh, Edinburgh, UK.
概括
医疗保健资源分配中的机器学习会影响患者的同意,特别是使用不透明的算法. 了解同意挑战对于在医疗保健决策中实施道德的人工智能至关重要.
科学领域:
- 生物伦理学生物伦理学
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 机器学习 (ML) 在医疗保健中的伦理影响越来越受到审查,特别是在患者知情同意方面.
- 现有的生物伦理讨论主要集中在诊断和治疗建议中的ML上,忽视了它在资源分配中的作用.
- ML对医疗保健资源分配中的患者同意的影响仍未得到充分研究.
研究的目的:
- 确定患者同意在医疗保健资源分配中的相关性.
- 分析黑子ML算法如何影响资源分配中的知情同意.
- 探索在医疗保健资源分配中使用ML的伦理考虑和挑战.
主要方法:
- 关于生物伦理和健康信息学研究的文献综述.
- 在ML的背景下,对知情同意原则的概念分析.
- 检查ML算法中的不透明度及其对患者理解和同意的影响.
主要成果:
- 患者的同意在医疗保健资源分配的多个点上是相关的.
- 黑子ML算法对获得真正知情同意提出了重大挑战.
- 在ML系统中的不透明性可以成为患者同意和信任的重大障碍.
结论:
- 伦理框架必须适应以解决医疗保健资源分配中的ML问题.
- 在ML中,透明度和可解释性对于维护患者自主性至关重要.
- 需要进一步的研究来制定人工智能驱动的医疗保健资源分配同意的实际指南.
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