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.

翻译：临床共情对患者护理至关重要，但医生需要在临床实践的认知与情感约束下，持续平衡情感温度与事实准确性。本研究探讨了大语言模型如何作为共情编辑器，通过优化医生书面回应来提升共情语调，同时保留基础医疗信息。更重要的是，我们引入了新颖的量化指标——共情排序分数与医疗事实核查分数，以系统评估回应的情感质量与事实质量。实验结果表明，相较于完全由大语言模型生成的输出，经大语言模型编辑的回应在保持事实准确性的同时，显著提升了感知共情度。这些发现表明，将大语言模型作为编辑助手而非自主生成器，为构建更具共情力且可信赖的AI辅助医疗沟通提供了更安全、更有效的路径。