MentalSeek-Dx: 向现实世界精神病诊断的渐进性假说-演绎推理迈进
MentalSeek-Dx: Towards Progressive Hypothetico-Deductive Reasoning for Real-world Psychiatric Diagnosis
- School of Computer Science Chongqing University（重庆大学计算机学院）
- MAIS Institute of Automation Chinese Academy of Sciences（中国科学院自动化研究所MAIS研究所）
- The First Affiliated Hospital of Chongqing Medical University（重庆医科大学第一附属医院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
MentalSeek-Dx通过监督轨迹构建和课程强化学习，实现了在现实世界精神病诊断中的渐进性假说-演绎推理，以提升临床诊断的可靠性。
AI中文摘要：
精神健康障碍代表了日益严峻的全球公共卫生挑战。尽管大型语言模型（LLMs）在精神病评估中展现出潜力，但其临床实用性严重受限于缺乏生态效度和精细诊断监督的基准。为弥合这一差距，我们引入了MentalDx Bench，这是首个专注于现实世界临床环境中疾病层面精神病诊断的基准。该基准包含712份去标识的电子健康记录，由持有执照的精神科医生根据ICD-11指南标注，涵盖16个诊断类别中的76种疾病。对18个LLMs的评估揭示了关键的范式错位：在粗粒度诊断分类上表现强劲，但在疾病层面诊断上系统性失败，凸显了基于模式的建模与临床假说-演绎推理之间的差距。为此，我们提出了MentalSeek-Dx，一个专门用于医学的LLM，通过监督轨迹构建和基于课程的强化学习训练，以内部化这一临床推理过程。在MentalDx Bench上的实验表明，MentalSeek-Dx仅使用14B参数即可实现最先进的性能，建立了具有临床依据的可靠精神病诊断框架。
英文摘要：
Mental health disorders represent a burgeoning global public health challenge. While Large Language Models (LLMs) have demonstrated potential in psychiatric assessment, their clinical utility is severely constrained by benchmarks that lack ecological validity and fine-grained diagnostic supervision. To bridge this gap, we introduce \textbf{MentalDx Bench}, the first benchmark dedicated to disorder-level psychiatric diagnosis within real-world clinical settings. Comprising 712 de-identified electronic health records annotated by board-certified psychiatrists under ICD-11 guidelines, the benchmark covers 76 disorders across 16 diagnostic categories. Evaluation of 18 LLMs reveals a critical \textit{paradigm misalignment}: strong performance at coarse diagnostic categorization contrasts with systematic failure at disorder-level diagnosis, underscoring a gap between pattern-based modeling and clinical hypothetico-deductive reasoning. In response, we propose \textbf{MentalSeek-Dx}, a medical-specialized LLM trained to internalize this clinical reasoning process through supervised trajectory construction and curriculum-based reinforcement learning. Experiments on MentalDx Bench demonstrate that MentalSeek-Dx achieves state-of-the-art (SOTA) performance with only 14B parameters, establishing a clinically grounded framework for reliable psychiatric diagnosis.
