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MedKP: Medical Dialogue with Knowledge Enhancement and Clinical Pathway Encoding

Core Concepts
MedKP framework enhances medical dialogue generation by integrating external knowledge and clinical pathway encoding, reducing hallucinations and improving response quality.
Introduction: Large Language Models (LLMs) have shown potential in the medical field, especially in online medical consultations. Challenges: LLMs face issues like hallucinations and lack of interpretability. Methodology: MedKP framework integrates external knowledge enhancement and internal clinical pathway encoding to improve response quality. Experiments: MedKP outperforms baselines on two datasets, demonstrating effectiveness in reducing hallucinations and improving response quality. Results: MedKP shows superiority in various metrics, highlighting its effectiveness in generating accurate medical responses. Ablation Study: Each component of MedKP significantly enhances performance, with the integration of all components yielding the best results. Conclusion: MedKP is a promising framework for enhancing medical dialogue generation using LLMs.
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.
"Large language models (LLMs) have demonstrated significant potential in the medical field." "Online medical consultation is probably the most suitable area for LLM application." "Extensive ablation studies underscore the contribution of individual components to the overall efficacy of our approach."

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by Jiageng Wu,X... at 03-12-2024

Deeper Inquiries

How can integrating external knowledge graphs enhance other applications beyond just medical dialogue generation


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