MedKP: Medical Dialogue with Knowledge Enhancement and Clinical Pathway Encoding
This work addresses the challenge of generating reliable medical consultation responses using LLMs, which could improve accessibility to precise and real-time medical assistance, though it is incremental as it builds on existing methods for knowledge integration.
The paper tackled the problem of inaccurate and hallucinated responses in medical dialogue generation by LLMs, introducing the MedKP framework that integrates external knowledge enhancement and clinical pathway encoding, achieving a new state-of-the-art on two datasets and reducing hallucinations.
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.