用医生思维链提升AI诊断，减少误判和幻觉。
Thinking Like a Clinician: A Cognitive AI Agent for Clinical Diagnosis via Panoramic Profiling and Adversarial Debate
- 模拟医生记忆锚定、导航和验证三阶段认知流程
- 在两个真实数据集上准确率和逻辑一致性均达顶尖水平
- 适合医疗AI研究者与临床决策支持系统开发者
大型语言模型在临床决策支持中面临处理非结构化电子健康记录时的“视野狭窄”和诊断幻觉问题。为此，我们提出一种基于链式结构的临床推理框架DxChain，将诊断流程转化为迭代过程，模仿医生的认知轨迹，包含“记忆锚定”、“导航”和“验证”三个阶段。DxChain引入三项关键方法创新：（i）采用“画像先行-规划”范式，通过建立全景患者基线缓解冷启动幻觉；（ii）设计医学思维树（Med-ToT）算法，实现前瞻式规划与资源感知导航；（iii）采用“天使-魔鬼”对抗辩论的辩证诊断验证机制，解决复杂证据冲突。在两个真实世界基准MIMIC-IV-Ext Cardiac Disease和MIMIC-IV-Ext CDM上评估，DxChain在诊断准确率与逻辑一致性方面均达到当前最优表现，提供了一个模块化且可靠的下一代临床AI架构。代码地址：https://anonymous.4open.science/r/Dx-Chain。
原文摘要 · Abstract (English)
The application of large language models (LLMs) in clinical decision support faces significant challenges of "tunnel vision" and diagnostic hallucinations present in their processing unstructured electronic health records (EHRs). To address these challenges, we propose a novel chain-based clinical reasoning framework, called DxChain, which transforms the diagnostic workflow into an iterative process by mirroring a clinician's cognitive trajectory that consists of "Memory Anchoring", "Navigation" and "Verification" phases. DxChain introduces three key methodological innovations to elicit the potential of LLM: (i) a Profile-Then-Plan paradigm to mitigate cold-start hallucinations by establishing a panoramic patient baseline, (ii) a Medical Tree-of-Thoughts (Med-ToT) algorithm for strategic look ahead planning and resource aware navigation, and (iii) a Dialectical Diagnostic Verification procedure utilizing "Angel-Devil" adversarial debates to resolve complex evidence conflicts. Evaluated on two real world benchmarks, MIMIC-IV-Ext Cardiac Disease and MIMIC-IV-Ext CDM, DxChain achieves state-of-the-art performances in both diagnostic accuracy and logical consistency, offering a modular and reliable architecture for next-generation clinical AI. The code is at https://anonymous.4open.science/r/Dx-Chain.
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