用证据引导大模型提升抑郁症诊断的可信度和透明度。
Leveraging Evidence-Guided LLMs to Enhance Trustworthy Depression Diagnosis
- 通过证据提取与诊断逻辑交替，生成符合DSM-5标准的结构化诊断假设。
- 在D4数据集上，诊断准确率最高提升45%，置信度评分提高36%。
- 适合医疗AI研发者、精神科医生及关注可解释AI的临床决策研究者。
大型语言模型（LLMs）在自动化临床诊断方面展现潜力，但其决策过程不透明且与诊断标准对齐不足，限制了信任度和临床应用。本文提出两阶段诊断框架以增强透明性、可信度和可靠性。首先，引入证据引导诊断推理（EGDR），通过在证据抽取与基于DSM-5标准的逻辑推理之间交替，引导模型生成结构化诊断假设。其次，提出诊断置信度评分（DCS）模块，通过知识归属得分（KAS）和逻辑一致性得分（LCS）两个可解释指标评估诊断的事实准确性和逻辑一致性。在带有伪标签的D4数据集上，EGDR在五种LLM中均优于直接上下文提示和思维链（CoT）方法。例如，在OpenBioLLM上，准确率从0.31（直接法）提升至0.76，DCS从0.50增至0.67；在MedLlama上，DCS从0.58（CoT）升至0.77。整体上，相比基线方法，EGDR实现最高45%的准确率提升和36%的DCS提升，为可信的AI辅助诊断提供可落地的临床基础。
原文摘要 · Abstract (English)
Large language models (LLMs) show promise in automating clinical diagnosis, yet their non-transparent decision-making and limited alignment with diagnostic standards hinder trust and clinical adoption. We address this challenge by proposing a two-stage diagnostic framework that enhances transparency, trustworthiness, and reliability. First, we introduce Evidence-Guided Diagnostic Reasoning (EGDR), which guides LLMs to generate structured diagnostic hypotheses by interleaving evidence extraction with logical reasoning grounded in DSM-5 criteria. Second, we propose a Diagnosis Confidence Scoring (DCS) module that evaluates the factual accuracy and logical consistency of generated diagnoses through two interpretable metrics: the Knowledge Attribution Score (KAS) and the Logic Consistency Score (LCS). Evaluated on the D4 dataset with pseudo-labels, EGDR outperforms direct in-context prompting and Chain-of-Thought (CoT) across five LLMs. For instance, on OpenBioLLM, EGDR improves accuracy from 0.31 (Direct) to 0.76 and increases DCS from 0.50 to 0.67. On MedLlama, DCS rises from 0.58 (CoT) to 0.77. Overall, EGDR yields up to +45% accuracy and +36% DCS gains over baseline methods, offering a clinically grounded, interpretable foundation for trustworthy AI-assisted diagnosis.
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