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.

通过结合医学知识图谱和内部临床路径编码，本研究提出了医学对话增强与临床路径编码（MedKP）框架，通过综合评估指标在两个大规模实际在线医学咨询数据集上的实验证明，MedKP 超过了多个基准系统，减少了产生幻觉的情况，达到了新的最先进水平，进一步的剔除研究揭示了 MedKP 每个组件的有效性，促进了可靠自动化的医学咨询响应的发展，从而拓宽了精确实时医学辅助的潜在可及性。