With appropriate data selection and training techniques, Large Language Models (LLMs) have demonstrated exceptional success in various medical examinations and multiple-choice questions. However, the application of LLMs in medical dialogue generation-a task more closely aligned with actual medical practice-has been less explored. This gap is attributed to the insufficient medical knowledge of LLMs, which leads to inaccuracies and hallucinated information in the generated medical responses. In this work, we introduce the Medical dialogue with Knowledge enhancement and clinical Pathway encoding (MedKP) framework, which integrates an external knowledge enhancement module through a medical knowledge graph and an internal clinical pathway encoding via medical entities and physician actions. Evaluated with comprehensive metrics, our experiments on two large-scale, real-world online medical consultation datasets (MedDG and KaMed) demonstrate that MedKP surpasses multiple baselines and mitigates the incidence of hallucinations, achieving a new state-of-the-art. Extensive ablation studies further reveal the effectiveness of each component of MedKP. This enhancement advances the development of reliable, automated medical consultation responses using LLMs, thereby broadening the potential accessibility of precise and real-time medical assistance.

翻译：通过适当的数据选择与训练技术，大语言模型在各类医学考试及多项选择题中已展现出卓越成效。然而，大语言模型在更贴近实际临床实践的医疗对话生成任务中的应用尚待深入探索。这一差距归因于大语言模型医学知识储备不足，导致生成的医疗回复存在不准确性与幻觉信息。本文提出医疗对话知识增强与临床路径编码框架（MedKP），该框架通过医学知识图谱集成外部知识增强模块，并借助医学实体与医生行为实现内部临床路径编码。基于两个大规模真实在线医疗咨询数据集（MedDG与KaMed）的全面指标评估表明，MedKP超越多个基线模型，有效减轻幻觉现象，达到当前最优性能。大量消融实验进一步揭示了MedKP各组件的有效性。该研究推进了基于大语言模型开发可靠、自动化医疗咨询回复的进程，从而拓展精准实时医疗辅助的潜在可及性。