Medical Dialogue Generation via Intuitive-then-Analytical Differential Diagnosis
This work addresses the need for more practical medical dialogue systems that can assist clinicians and patients by providing transparent diagnostic processes, though it appears incremental by building on existing retrieval and graph methods.
The paper tackles the problem of generating medical dialogues by modeling differential diagnosis, which is often overlooked, and proposes the IADDx framework that combines intuitive retrieval and analytic graph refinement to improve diagnosis accuracy, achieving validated efficacy on two datasets.
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.