从生成到协作：利用LLMs编辑以增强医疗中的同理心
From Generation to Collaboration: Using LLMs to Edit for Empathy in Healthcare
AI总结：
本研究利用LLMs作为编辑工具，通过优化医生书面回应增强医疗中的同理心，同时保持事实准确性，提出新的评估指标并验证其有效性。
摘要：
临床同理心对于患者护理至关重要，但医生需要在临床实践中不断平衡情感温暖与事实精确性，以应对认知和情感限制。本研究探讨了大型语言模型（LLMs）如何作为同理心编辑器，通过优化医生的书面回应来增强同理心语气，同时保持底层医疗信息。更重要的是，我们引入了新的定量指标，即同理心排名分数和医学事实检查分数，以系统评估回应的情感和事实质量。实验结果表明，LLM编辑后的回应在增强感知同理心的同时，比完全由LLM生成的回应更能保持事实准确性。这些发现表明，将LLM用作编辑助手，而不是自主生成器，为实现具有同理心和可信度的AI辅助医疗沟通提供了一条更安全、更有效的方法。
英文摘要：
Clinical empathy is essential for patient care, but physicians need continually balance emotional warmth with factual precision under the cognitive and emotional constraints of clinical practice. This study investigates how large language models (LLMs) can function as empathy editors, refining physicians' written responses to enhance empathetic tone while preserving underlying medical information. More importantly, we introduce novel quantitative metrics, an Empathy Ranking Score and a MedFactChecking Score to systematically assess both emotional and factual quality of the responses. Experimental results show that LLM edited responses significantly increase perceived empathy while preserving factual accuracy compared with fully LLM generated outputs. These findings suggest that using LLMs as editorial assistants, rather than autonomous generators, offers a safer, more effective pathway to empathetic and trustworthy AI-assisted healthcare communication.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
