RagMedge: Dense Retrieval Augmented Medical Dialogue Generation Towards Personalized Medical Knowledge Grounded Diagnosis

To develop a medical generative system, current research has primarily focused on integrating medical knowledge into language models (LMs) through training them on medical corpora or incorporating external medical resources, such as knowledge graphs and terminologies. However, approaches rely on implicit knowledge integration from textual narratives or standardized knowledge sources (e.g., Unified Medical Language System), making the acquired knowledge opaque and difficult to control during inference. This lack of transparency limits the traceability of knowledge accumulated from historical training data. To address these limitations, we propose constructing a personalized medical knowledge base that extracts discrete knowledge points from real doctor-patient dialogues and encodes them as semantically condensed vectors. Building on this, we introduce a novel dense retrieval-augmented medical dialogue generation framework, called RagMedge, which retrieves relevant knowledge embeddings conditioned on the input context and incorporates them into response generation. Experimental results demonstrate that RagMedge achieves state-of-the-art performance, highlighting the effectiveness of leveraging personalized medical knowledge for improved dialogue generation.