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将专家知识整合到大型语言模型中可以提高精神病学推理和诊断的性能

Karthik V Sarma¹, Kaitlin E Hanss², Andrew J M Halls²

¹Department of Psychiatry and Behavioral Sciences, University of California San Francisco, 675 18th Street, San Francisco, CA 94143, USA; Bakar Computational Health Sciences Institute, University of California San Francisco, 550 16th Street, San Francisco, CA 94143, USA.

Psychiatry research

|November 21, 2025

概括

将专家推理与大型语言模型 (LLM) 整合起来,提高了精神病诊断的准确性. 决策树减少了过度诊断,提高了行为健康环境中的LLM绩效.

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) 显示出在协助精神病诊断方面的潜力.
在临床环境中评估LLM绩效需要严格的评估.
结构化临床推理对LLM诊断准确性的影响尚不清楚.

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提示与精神病诊断的LLM辅助推理.

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.
通过直接和决策树指导的方法,提醒了三个带有小幅图的LLM.
使用正预测值 (PPV),灵敏度和F1统计数据来评估性能.

主要成果:

直接LLM提示导致高灵敏度但低的积极预测值 (PPV),表明过度诊断.
使用决策树显著增加了PPV (从40.4%增加到65.3%),敏感度略有下降 (最佳模型从76.7%降至70.9%).
在大多数实验中,决策树在统计学上提高了PPV和F1分数,同时略有降低了灵敏度.

结论:

直接LLM提示精神病诊断导致过度诊断.
整合来自专家的决策树可以通过减少过度诊断来提高LLM诊断性能.
通过LLM辅助推理,可以改善行为健康的临床决策支持工具.

关键词:

人工智能的人工智能是人工智能.在DSM中,它是DSM.支持决定的决定支持.诊断诊断诊断诊断诊断生成型的人工智能 (GAI) 是一种人工智能.精神病学是一个精神病学.推理推理是一种推理.

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