将专家知识集成到大型语言模型中,提高了精神病学推理和诊断的性能
medRxiv : the preprint server for health sciences
|August 8, 2025
概括
大型语言模型 (LLM) 在精神病诊断方面表现有前途,但需要专家推理集成. 使用决策树通过减少过度诊断显著提高了诊断准确性,提高了LLM在行为健康中的实用性.
科学领域:
- 人工智能在医学中的应用
- 计算精神病学是一种计算精神病学.
- 临床决策支持系统 临床决策支持系统
背景情况:
- 大型语言模型 (LLM) 越来越多地被用于临床应用.
- 准确的精神病诊断是复杂的,并且从结构化的推理中获益.
- 评估LLM在诊断任务中的表现对于潜在的整合至关重要.
研究的目的:
- 通过使用临床病例 vignettes 来评估常见LLM的诊断性能.
- 调查整合专家衍生诊断决策树对LLM绩效的影响.
- 为了比较直接的LLM提示与决策树辅助诊断.
主要方法:
- 从DSM-5-TR资源中获取临床病例简介和诊断.
- 为LLM实施开发和完善诊断决策树.
- 促使三个LLM与没有决策树生成精神病诊断.
- 使用正预测值 (PPV),灵敏度和F统计数据来评估性能.
主要成果:
- 直接LLM提示 (gpt-4o) 实现了77.6%的灵敏度和43.3%的PPV.
- 决策树集成显著提高了PPV至65.3%,保持了灵敏度 (71.8%).
- 决策树在大多数实验中改善了F统计,并减少了过度诊断.
结论:
- 通过决策树集成来自专家的推理,提高了精神病诊断中的LLM绩效.
- 这种方法主要是减轻过度诊断,这是直接LLM提示的关键限制.
- 用临床推理增强的基于LLM的工具显示了改善行为健康诊断的潜力.
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