Medical dialogue systems have attracted growing research attention as they have the potential to provide rapid diagnoses, treatment plans, and health consultations. In medical dialogues, a proper diagnosis is crucial as it establishes the foundation for future consultations. Clinicians typically employ both intuitive and analytic reasoning to formulate a differential diagnosis. This reasoning process hypothesizes and verifies a variety of possible diseases and strives to generate a comprehensive and rigorous diagnosis. However, recent studies on medical dialogue generation have overlooked the significance of modeling a differential diagnosis, which hinders the practical application of these systems. To address the above issue, we propose a medical dialogue generation framework with the Intuitive-then-Analytic Differential Diagnosis (IADDx). Our method starts with a differential diagnosis via retrieval-based intuitive association and subsequently refines it through a graph-enhanced analytic procedure. The resulting differential diagnosis is then used to retrieve medical knowledge and guide response generation. Experimental results on two datasets validate the efficacy of our method. Besides, we demonstrate how our framework assists both clinicians and patients in understanding the diagnostic process, for instance, by producing intermediate results and graph-based diagnosis paths.

翻译：医疗对话系统因其在快速诊断、治疗方案制定及健康咨询方面的潜力而受到日益增长的研究关注。在医疗对话中，准确的诊断至关重要，因为它构成了后续咨询的基础。临床医生通常结合直觉推理与分析推理来形成鉴别诊断。这种推理过程假设并验证多种可能的疾病，力求生成全面且严谨的诊断。然而，近期关于医疗对话生成的研究忽视了鉴别诊断建模的重要性，这阻碍了这些系统的实际应用。为解决上述问题，我们提出了一种基于直觉-分析鉴别诊断（IADDx）的医疗对话生成框架。我们的方法首先通过基于检索的直觉关联进行鉴别诊断，随后通过图增强的分析过程对其进行优化。由此产生的鉴别诊断结果被用于检索医学知识并指导响应生成。在两个数据集上的实验结果验证了该方法的有效性。此外，我们展示了该框架如何通过生成中间结果和基于图的诊断路径，帮助临床医生和患者理解诊断过程。