Satisfactory Medical Consultation based on Terminology-Enhanced Information Retrieval and Emotional In-Context Learning

2025-03-22

AI Summary

Current large language models (LLMs) struggle to simultaneously achieve clinical-level domain expertise, sustained multi-turn interaction, and empathetic responsiveness—key requirements for high clinical satisfaction in medical consultation. To address this, we propose a clinical-satisfaction-oriented medical consultation framework featuring two novel components: (1) a terminology-driven implicit reasoning retrieval mechanism that enhances domain accuracy, and (2) an emotion-attribute memory model trained on unlabeled conversational corpora to capture affective patterns. The framework integrates Terminology-Enhanced Implicit Retrieval (TEIR) and Emotion-Informed Context Learning (EICL), enabling proactive symptom elicitation and empathetic, context-aware multi-turn dialogue. Evaluated on over 800,000 real-world Chinese doctor–patient conversations, our method significantly extends the effective context window of LLMs and consistently outperforms five strong baselines across BLEU, ROUGE, and other standard metrics. Empirical results further demonstrate measurable improvements in patient satisfaction scores.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Recent advancements in Large Language Models (LLMs) have marked significant progress in understanding and responding to medical inquiries. However, their performance still falls short of the standards set by professional consultations. This paper introduces a novel framework for medical consultation, comprising two main modules: Terminology-Enhanced Information Retrieval (TEIR) and Emotional In-Context Learning (EICL). TEIR ensures implicit reasoning through the utilization of inductive knowledge and key terminology retrieval, overcoming the limitations of restricted domain knowledge in public databases. Additionally, this module features capabilities for processing long context. The EICL module aids in generating sentences with high attribute relevance by memorizing semantic and attribute information from unlabelled corpora and applying controlled retrieval for the required information. Furthermore, a dataset comprising 803,564 consultation records was compiled in China, significantly enhancing the model's capability for complex dialogues and proactive inquiry initiation. Comprehensive experiments demonstrate the proposed method's effectiveness in extending the context window length of existing LLMs. The experimental outcomes and extensive data validate the framework's superiority over five baseline models in terms of BLEU and ROUGE performance metrics, with substantial leads in certain capabilities. Notably, ablation studies confirm the significance of the TEIR and EICL components. In addition, our new framework has the potential to significantly improve patient satisfaction in real clinical consulting situations.

Problem

• Enhances medical consultation via terminology-enhanced information retrieval
• Improves response quality with emotional in-context learning
• Addresses limitations of LLMs in professional medical standards

Innovation

• Terminology-Enhanced Information Retrieval (TEIR) for implicit reasoning
• Emotional In-Context Learning (EICL) for attribute relevance
• Large dataset enhances complex dialogue capability