大模型如何辅助医疗决策？从他汀类药用法说起
Treatment, evidence, imitation, and chat
- 用大模型处理医疗决策，需结合实验与观察数据
- 仅模仿医生对话无法解决真实治疗问题
- 挑战来自伦理与观察假设，关乎循证医学实践
大型语言模型被认为有潜力辅助医疗决策。本文以患者核心决策任务——治疗问题为切入点，探讨其与临床医生协作的解决路径。在循证医学框架下，分析了实验与观察数据的应用方式。接着讨论聊天场景与治疗问题的区别，尤其指出单纯模仿无法解决真正的治疗问题，尽管模仿仍有价值。进一步分析基于大模型的系统如何训练以应对治疗问题，强调主要挑战源于实验伦理及观察数据的假设依赖。最后联系循证医学，探讨这些挑战对医学研究社区的启示。全文以他汀类药物为例进行说明。
原文摘要 · Abstract (English)
Large language models are thought to have the potential to aid in medical decision making. This work investigates the degree to which this might be the case. We start with the treatment problem, the patient's core medical decision-making task, which is solved in collaboration with a clinician. We discuss different approaches to solving it, including, within evidence-based medicine, experimental and observational data. We then discuss the chat problem, and how this differs from the treatment problem -- in particular with respect to imitation (and how imitation alone cannot solve the true treatment problem, although this does not mean it is not useful). We then discuss how a large-language-model-based system might be trained to solve the treatment problem, highlighting that the major challenges relate to the ethics of experimentation and the assumptions associated with observation. We finally discuss how these challenges relate to evidence-based medicine and how this might inform the efforts of the medical research community to solve the treatment problem. Throughout, we illustrate our arguments with the cholesterol medications, statins.
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