基于证据的医学中大语言模型的认识和伦理限制:从知识到判断
1Department of Philosophy, Peking University, Beijing, China.
Frontiers in digital health
|February 5, 2026
概括
一般的大型语言模型 (LLM) 在医学上表现有希望,但在基于证据的医学任务中存在局限性. 直接应用在准确性,可验证性和责任性方面面临挑战,需要仔细整合到临床实践中.
科学领域:
- 人工智能在医学中的应用
- 医疗信息学 医疗信息学
- 基于证据的医学基于证据的医学.
背景情况:
- 一般的大型语言模型 (LLM) 提供了将AI整合到医疗保健中的潜力.
- 在将LLM应用于临床证据推断和任务方面存在挑战.
- 本研究探讨了基于证据的医学 (EBM) 中的LLM能力和局限性.
研究的目的:
- 在EBM任务中实证地分析当前的通用LLM的能力边界.
- 为LLMs在临床证据推断中的局限性提供哲学反思.
- 在证据生成,合成和临床判断方面评估LLM绩效.
主要方法:
- 在基准设置中评估了三个通用LLM (ChatGPT,DeepSeek,Gemini).
- 在两个临床场景中使用多维标签评估性能.
- 从认识论的角度分析经验结果.
主要成果:
- 对于EBM的核心任务,LLM产生了连贯的,在医学上可信的输出.
- 确定的局限性包括数值不准确,无法验证的来源,不一致的合成和临床责任归因的弱点.
- 哲学分析揭示了脱体,非制度化和脱实践化的风险.
结论:
- 一般LLM对临床证据任务的直接应用存在局限性.
- 目前的法律法规缺乏具体的参与,制度规范参与和问责制.
- 未来的医疗人工智能需要对现实世界的数据,集成的工作流程和明确的人类-人工智能责任框架进行接地.
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