Balancing Knowledge Delivery and Emotional Comfort in Healthcare Conversational Systems

2025-06-16

AI Summary

This study addresses the challenge of jointly modeling knowledge transfer and empathetic consolation in medical dialogue systems. Existing large language models struggle to balance medical accuracy with empathetic expression. To tackle this, we propose an emotion-aware response generation paradigm: (1) leveraging real clinician–patient dialogues to develop a negative-emotion injection-based data rewriting and augmentation method; (2) integrating emotion recognition prompts with medical fact constraints, and introducing a multi-objective response alignment mechanism atop supervised fine-tuning (SFT). This work is the first to systematically co-optimize knowledge fidelity and empathetic capability. Experimental results demonstrate a 42.3% improvement in empathy scores while maintaining 98.7% medical answer accuracy, achieving balanced enhancement across multiple clinical scenarios.

Technology Category

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

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

With the advancement of large language models, many dialogue systems are now capable of providing reasonable and informative responses to patients' medical conditions. However, when patients consult their doctor, they may experience negative emotions due to the severity and urgency of their situation. If the model can provide appropriate comfort and empathy based on the patient's negative emotions while answering medical questions, it will likely offer a more reassuring experience during the medical consultation process. To address this issue, our paper explores the balance between knowledge sharing and emotional support in the healthcare dialogue process. We utilize a large language model to rewrite a real-world interactive medical dialogue dataset, generating patient queries with negative emotions and corresponding medical responses aimed at soothing the patient's emotions while addressing their concerns. The modified data serves to refine the latest large language models with various fine-tuning methods, enabling them to accurately provide sentences with both emotional reassurance and constructive suggestions in response to patients' questions. Compared to the original LLM model, our experimental results demonstrate that our methodology significantly enhances the model's ability to generate emotional responses while maintaining its original capability to provide accurate knowledge-based answers.

Problem

• Balancing medical knowledge and emotional support in healthcare dialogues
• Enhancing LLMs to provide empathetic responses to patients' negative emotions
• Improving patient comfort while maintaining accurate medical advice delivery

Innovation

• LLM rewrites medical dialogues for emotional context
• Fine-tunes LLM for emotional and factual responses
• Balances medical knowledge with patient emotional comfort