Conversational diagnosis requires multi-turn history-taking, where an agent asks clarifying questions to refine differential diagnoses under incomplete information. Existing approaches often rely on the parametric knowledge of a model or assume that patients provide rich and concrete information, which is unrealistic. To address these limitations, we propose a conversational diagnosis system that explores a diagnostic knowledge graph to reason in two steps: (i) generating diagnostic hypotheses from the dialogue context, and (ii) verifying hypotheses through clarifying questions, which are repeated until a final diagnosis is reached. Since evaluating the system requires a realistic patient simulator that responds to the system's questions, we adopt a well-established simulator along with patient profiles from MIMIC-IV. We further adapt it to describe symptoms vaguely to reflect real-world patients during early clinical encounters. Experiments show improved diagnostic accuracy and efficiency over strong baselines, and evaluations by physicians support the realism of our simulator and the clinical utility of the generated questions. Our code will be released upon publication.

翻译：对话式诊断需要多轮病史采集，在此过程中，智能体通过提出澄清性问题，在不完整信息下逐步完善鉴别诊断。现有方法通常依赖模型的参数化知识，或假设患者能提供丰富且具体的信息，这在现实中并不成立。为应对这些局限，我们提出了一种对话式诊断系统，该系统通过探索诊断知识图谱进行两步推理：（i）从对话上下文中生成诊断假设，以及（ii）通过澄清性问题验证假设，该过程循环进行直至得出最终诊断。由于评估系统需要一个能响应系统提问的真实患者模拟器，我们采用了一个成熟的模拟器，并结合了来自MIMIC-IV的患者档案。我们进一步调整该模拟器，使其在描述症状时保持模糊性，以反映真实世界患者在早期临床接触中的情况。实验表明，与强基线相比，我们的系统在诊断准确性和效率上均有提升，且医师评估结果支持我们模拟器的真实性以及所生成问题的临床实用性。我们的代码将在论文发表时公开。