对医疗机器学习的有害认识学依赖及其道德含义
Giorgia Pozzi1, Stefan Buijsman2, Jeroen van den Hoven2
1Faculty of Technology, Policy and Management, Delft University of Technology, Delft, Netherlands g.pozzi@tudelft.nl.
临床专业人员可能会对医疗机器学习 (ML) 系统产生有害的依赖,从而造成道德风险. 设计人工的认识领域可以减轻这种依赖,改善医疗保健.
科学领域:
- 医疗信息学 医疗信息学
- 技术的哲学技术的哲学
- 临床伦理学 临床伦理学
背景情况:
- 机器学习 (ML) 系统越来越多地集成到临床决策环境中.
- 这些数字化的环境,称为人工认识领域,引发了重大的认识论和道德问题.
- 在生命关键的医疗保健中依赖ML需要了解用户依赖.
研究的目的:
- 在临床环境中定义对ML系统的认识学依赖.
- 确定这种依赖对医疗保健专业人员变得有害的条件.
- 分析有害的认识论依赖的认识论和道德后果.
主要方法:
- 在医学ML的背景下,认识论依赖的概念分析.
- 调查与无法进行现场评估相关的道德义务和风险.
- 检查对临床医生的职业地位的影响.
主要成果:
- 当临床医生无法批判性地评估ML输出并因不遵守而面临不合理的道德风险时,就会发生有害的认识系统依赖.
- 这种依赖性可能会破坏医疗从业者的专业自主权和道德地位.
- 人工认识领域的设计对于管理这些风险至关重要.
结论:
- 解决有害的认识系统依赖需要仔细考虑临床工作流程中的ML系统设计.
- 缓解策略应侧重于创建透明和可审计的人工认识领域.
- 优化利基设计可以帮助保持临床判断和道德责任.
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