在医学中使用自适应机器学习系统应该被归类为研究吗?
Robert Sparrow1, Joshua Hatherley1, Justin Oakley1
1Monash University.
医学中的自适应机器学习 (ML) 提供了持续的学习,但也引发了伦理问题. 这篇论文认为,临床实践中持续的ML学习应该被规范为医学研究,将患者视为研究对象.
科学领域:
- 医学伦理 医学伦理
- 人工智能在医学中的应用
- 监管科学 监管科学
背景情况:
- 医学中的自适应机器学习 (ML) 系统可以在实现后从新数据中学习.
- 目前的伦理讨论集中在调节不断发展的ML系统的"更新问题"上.
- 关于持续学习的先前伦理考虑在很大程度上仍未得到解决.
研究的目的:
- 检查医疗ML系统部署后的持续学习是否应该被归类和规范为医学研究.
- 为在临床环境中进行的ML学习进行重新分类辩论.
主要方法:
- 对医疗保健中的自适应机器学习系统的伦理分析.
- 基于持续学习和患者参与的性质的论证.
- 审查现有的监管框架和道德原则.
主要成果:
- 有强有力的初见证据表明,医学ML系统中的持续学习构成了研究.
- 使用此类系统接受治疗的个人应被视为研究对象.
- 目前的监管方法可能不足以适应性ML系统.
结论:
- 医疗ML系统的持续学习需要对其监管状况进行重新评估.
- 在适应性ML部署期间将患者视为研究对象,确保了伦理监督.
- 这种重构对于人工智能驱动的医疗保健中负责任的创新至关重要.
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