用多智能体系统提升罗马尼亚语医生的沟通质量
Dr.Copilot: A Multi-Agent Prompt Optimized Assistant for Improving Patient-Doctor Communication in Romanian
- 三智能体系统自动优化提示，评估17个沟通维度
- 实测41名医生使用后反馈和回复质量显著提升
- 首个在罗马尼亚医疗场景落地的LLM辅助系统
基于文本的远程医疗日益普及，但医患沟通的质量常被重视程度超过临床准确性。为此，我们提出Dr. Copilot，一个面向罗马尼亚语医生的多智能体大语言模型系统，用于评估并提升其书面回复的表达质量。该系统不判断医学正确性，而是从17个可解释维度提供反馈。由三个经DSPy自动优化提示的LLM智能体构成，基于低资源罗马尼亚语数据训练，采用开源权重模型部署，在远程医疗平台中实现实时反馈。在41名医生的实证评估与真实部署中，用户评价和回复质量均显著改善，标志着首个在罗马尼亚医疗场景落地的大语言模型应用。
原文摘要 · Abstract (English)
Text-based telemedicine has become increasingly common, yet the quality of medical advice in doctor-patient interactions is often judged more on how advice is communicated rather than its clinical accuracy. To address this, we introduce Dr. Copilot , a multi-agent large language model (LLM) system that supports Romanian-speaking doctors by evaluating and enhancing the presentation quality of their written responses. Rather than assessing medical correctness, Dr. Copilot provides feedback along 17 interpretable axes. The system comprises of three LLM agents with prompts automatically optimized via DSPy. Designed with low-resource Romanian data and deployed using open-weight models, it delivers real-time specific feedback to doctors within a telemedicine platform. Empirical evaluations and live deployment with 41 doctors show measurable improvements in user reviews and response quality, marking one of the first real-world deployments of LLMs in Romanian medical settings.
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