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.

Abstract

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.

Stats

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.

Quotes

"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."

Key Insights Distilled From

MedKP

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

https://arxiv.org/pdf/2403.06611.pdf

Deeper Inquiries

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

外部知識グラフを統合することで、他のアプリケーションにもさまざまな利点があります。例えば、情報検索エンジンやオンライン教育プラットフォームでは、外部知識グラフを活用してユーザーにより適切な情報や学習コンテンツを提供することが可能です。また、ビジネス分野では市場調査や競合分析などの領域でも外部知識グラフを活用することで意思決定のサポートが向上します。

What are some potential ethical considerations when using large language models like GPT-4 for generating sensitive content such as healthcare information

GPT-4のような大規模言語モデルを使用して医療情報などの機密性の高いコンテンツを生成する際にはいくつかの倫理的考慮事項があります。第一に、プライバシー保護が重要です。患者情報や個人特定可能なデータは厳格に管理される必要があります。また、生成されたコンテンツが正確で信頼性があることも重要です。医療関連の情報は命に関わる場合もあるため、間違った情報や診断は深刻な影響を及ぼす可能性があります。

How can advancements in natural language processing technology impact patient-doctor interactions in online consultations

自然言語処理技術の進歩はオンライン相談で患者と医師の対話に大きな影響を与えています。例えば、会話型AIチャットボットを介した自動応答システムは効率的かつ迅速な医療支援を提供し、待ち時間や手間を削減します。また、自然言語処理技術は文書作成や記録管理も改善し、精度と効率性を向上させます。これらの技術革新によりオンライン相談では質問への回答速度や品質が向上し、患者体験全体も改善される可能性があります。