用系统思维重构医疗AI，让模型更懂临床需求
From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Automation of Patient Care
- 从数据驱动转向目的驱动，结合临床理论与真实场景
- 强调数据生成源头与自动化目标的双向理解
- 适合医疗AI研究者和临床系统设计者参考
本文反思当前人工智能驱动患者护理自动化的数据驱动建模范式。指出将现有真实世界患者数据用于机器学习未必最优，可能带来不良临床后果。回顾数据分析发展史，说明数据驱动范式兴起的原因，并展望系统思维与临床领域理论如何补充现有建模范式，以实现以人为本的成果。呼吁建立基于临床理论和现实操作情境的社会技术背景的目的驱动机器学习范式。认为理解现有患者数据的效用需同时关注上游数据生成过程与下游自动化目标。这一目的驱动视角为医疗AI系统开发带来新方法机遇，具有推动护理自动化的重要潜力。
原文摘要 · Abstract (English)
In this work, we reflect on the data-driven modeling paradigm that is gaining ground in AI-driven automation of patient care. We argue that the repurposing of existing real-world patient datasets for machine learning may not always represent an optimal approach to model development as it could lead to undesirable outcomes in patient care. We reflect on the history of data analysis to explain how the data-driven paradigm rose to popularity, and we envision ways in which systems thinking and clinical domain theory could complement the existing model development approaches in reaching human-centric outcomes. We call for a purpose-driven machine learning paradigm that is grounded in clinical theory and the sociotechnical realities of real-world operational contexts. We argue that understanding the utility of existing patient datasets requires looking in two directions: upstream towards the data generation, and downstream towards the automation objectives. This purpose-driven perspective to AI system development opens up new methodological opportunities and holds promise for AI automation of patient care.
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