学术机器学习研究人员对医疗保健算法开发的伦理观点:定性研究
Max Kasun1, Katie Ryan1, Jodi Paik1
1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, United States.
Journal of the American Medical Informatics Association : JAMIA
|December 9, 2023
概括
学术机器学习 (ML) 研究人员发现他们的工作具有道德意义. 他们强调了数据和算法开发方面的担忧,并倡导在医疗人工智能领域进行更好的跨学科培训和综合伦理方法.
科学领域:
- 医疗人工智能 医疗人工智能
- 医疗保健中的机器学习
- 临床决策支持系统 临床决策支持系统
背景情况:
- 在开发用于临床护理的机器学习 (ML) 工具时,道德考虑至关重要.
- 了解医学ML研究人员的观点对于负责任的创新至关重要.
- 将ML工具转化为医疗实践需要对道德挑战进行彻底审查.
研究的目的:
- 描述参与开发临床护理ML工具的学术机器学习研究人员的伦理考虑.
- 确定在医学ML算法的开发和培训过程中遇到的具体伦理挑战.
- 探索研究人员对解决医疗AI道德问题的必要支持和培训的看法.
主要方法:
- 定性描述性研究设计.
- 深入的,半结构化的采访与10个ML医学研究人员.
- 传统的定性内容分析,以确定新兴的主题.
主要成果:
- 所有接受采访的研究人员都承认他们的ML开发工作的伦理意义.
- 对数据采样/标签 (偏见,有效性,完整性) 和算法培训/测试 (可重复性,目标选择) 提出了担忧.
- 参与者强调需要加强跨学科培训和协调伦理整合.
结论:
- 医学ML研究人员面临着独特的伦理标准和技术挑战,影响临床可接受性.
- 在诸如偏差缓解,数据完整性和可重复性等领域需要增加支持.
- 解决这些伦理考虑是成功开发和整合医疗ML工具的关键.
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