用大模型+逻辑编程做可解释的精神病诊断，避免误诊。
Large Language Models for Interpretable Mental Health Diagnosis
- 大模型生成诊断规则，再由逻辑引擎推理
- 专家审核后准确率显著高于纯大模型
- 适合临床医生、医疗AI开发者使用
我们提出一种用于精神健康诊断的临床决策支持系统（CDSS），结合大语言模型（LLM）与约束逻辑编程（CLP）。该系统的重要性源于精神健康诊断手册的高度复杂性及误诊风险。我们的CDSS利用LLM将诊断手册转化为逻辑程序，并通过现成的CLP引擎求解，根据编码规则和患者数据查询诊断结果。通过让领域专家检查并修改LLM生成的逻辑程序，确保诊断既准确又可解释。实验对比了两种基线方法：仅使用LLM进行诊断，以及使用LLM生成的逻辑程序但无专家审查。结果表明，尽管LLM能有效生成候选逻辑程序，但必须经专家审核和修改才能保证与官方诊断手册的一致性。此外，直接使用患者数据于LLM引发伦理担忧，凸显了我们所提混合方法的安全必要性。
原文摘要 · Abstract (English)
We propose a clinical decision support system (CDSS) for mental health diagnosis that combines the strengths of large language models (LLMs) and constraint logic programming (CLP). Having a CDSS is important because of the high complexity of diagnostic manuals used by mental health professionals and the danger of diagnostic errors. Our CDSS is a software tool that uses an LLM to translate diagnostic manuals to a logic program and solves the program using an off-the-shelf CLP engine to query a patient's diagnosis based on the encoded rules and provided data. By giving domain experts the opportunity to inspect the LLM-generated logic program, and making modifications when needed, our CDSS ensures that the diagnosis is not only accurate but also interpretable. We experimentally compare it with two baseline approaches of using LLMs: diagnosing patients using the LLM-only approach, and using the LLM-generated logic program but without expert inspection. The results show that, while LLMs are extremely useful in generating candidate logic programs, these programs still require expert inspection and modification to guarantee faithfulness to the official diagnostic manuals. Additionally, ethical concerns arise from the direct use of patient data in LLMs, underscoring the need for a safer hybrid approach like our proposed method.
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