用因果机器学习提升医疗决策系统，让AI推理更可信、更易用。
Integrating Causal Machine Learning into Clinical Decision Support Systems: Insights from Literature and Practice
- 基于临床医生访谈与文献综述，提炼出8项设计需求
- 提出7条原则与9个功能特征，强化人机协作与可解释性
- 关注医疗AI的伦理与监管，推动适应性认证机制
当前临床决策支持系统（CDSS）多依赖相关性进行预测，而因果机器学习（ML）为提升决策质量提供了可解释、治疗特定的推理路径。然而，现有研究偏重模型开发，忽视面向医生的界面设计。本文采用设计科学方法，结合结构化文献回顾与资深医师访谈，归纳出8项实证驱动的设计要求，提出7条设计原则与9项实用功能。结果为构建能提供因果洞察、无缝嵌入临床流程、增强信任与可用性的CDSS提供指导。同时揭示自动化、责任归属与监管之间的张力，强调需建立适应性认证机制以支持基于机器学习的医疗产品。
原文摘要 · Abstract (English)
Current clinical decision support systems (CDSSs) typically base their predictions on correlation, not causation. In recent years, causal machine learning (ML) has emerged as a promising way to improve decision-making with CDSSs by offering interpretable, treatment-specific reasoning. However, existing research often emphasizes model development rather than designing clinician-facing interfaces. To address this gap, we investigated how CDSSs based on causal ML should be designed to effectively support collaborative clinical decision-making. Using a design science research methodology, we conducted a structured literature review and interviewed experienced physicians. From these, we derived eight empirically grounded design requirements, developed seven design principles, and proposed nine practical design features. Our results establish guidance for designing CDSSs that deliver causal insights, integrate seamlessly into clinical workflows, and support trust, usability, and human-AI collaboration. We also reveal tensions around automation, responsibility, and regulation, highlighting the need for an adaptive certification process for ML-based medical products.
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