剖析医疗自适应机器学习系统的伦理风险，关注随时间与多机构的差异表现。
Diachronic and synchronic variation in the performance of adaptive machine learning systems: The ethical challenges
- 区分系统随时间演变（时序变化）与不同机构并行差异（同步变异）
- 揭示两类变异对诊疗质量、知情同意与公平性的潜在威胁
- 面向开发者、监管者与临床医生，提供伦理设计指南
机器学习具有在临床部署后持续响应新数据而进化的潜力，实现医学中的‘持续学习’。本文系统梳理了此类‘自适应’机器学习系统所引发但尚未被充分讨论的伦理问题。现有研究忽视了两种关键变异：时序演化（系统随时间变化）与同步变异（不同机构间同一算法的并行差异），且低估了后者的重要性。本文重点分析这两类变异对患者诊疗质量、知情同意及医疗公平性的挑战，并探讨系统设计中复杂的伦理权衡，旨在为机器学习开发者、医疗监管机构、医学信息学研究者及临床医生提供参考。
原文摘要 · Abstract (English)
Objectives: Machine learning (ML) has the potential to facilitate "continual learning" in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this paper, we provide a tutorial on the range of ethical issues raised by the use of such "adaptive" ML systems in medicine that have, thus far, been neglected in the literature. Target audience: The target audiences for this tutorial are the developers of machine learning AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians. Scope: Discussions of adaptive ML systems to date have overlooked the distinction between two sorts of variance that such systems may exhibit -- diachronic evolution (change over time) and synchronic variation (difference between cotemporaneous instantiations of the algorithm at different sites) -- and under-estimated the significance of the latter. We highlight the challenges that diachronic evolution and synchronic variation present for the quality of patient care, informed consent, and equity, and discuss the complex ethical trade-offs involved in the design of such systems.
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