轻量多智能体系统实现本地化医疗助手，隐私安全且响应快
Multi Agent based Medical Assistant for Edge Devices
- 采用小型专用智能体分工协作，资源占用低适合设备端运行
- 规划与调用任务平均RougeL得分分别达85.5和96.5，性能优异
- 适合注重隐私、离线可用的个人健康管理场景
大型动作模型（LAMs）虽推动了智能自动化，但在医疗领域因隐私担忧、延迟高及依赖网络而受限。本文提出一种基于多智能体的本地化医疗助手，通过小型化、任务专用的智能体优化资源使用，确保可扩展性与高性能。系统集成预约挂号、健康监测、服药提醒与每日健康报告等功能，作为一站式健康服务方案。基于Qwen Code Instruct 2.5 7B模型，规划智能体与调用智能体在任务中分别取得平均RougeL得分85.5与96.5，同时具备轻量化特性，适用于设备端部署。该方法融合本地化系统与多智能体架构优势，为以用户为中心的医疗解决方案提供新路径。
原文摘要 · Abstract (English)
Large Action Models (LAMs) have revolutionized intelligent automation, but their application in healthcare faces challenges due to privacy concerns, latency, and dependency on internet access. This report introduces an ondevice, multi-agent healthcare assistant that overcomes these limitations. The system utilizes smaller, task-specific agents to optimize resources, ensure scalability and high performance. Our proposed system acts as a one-stop solution for health care needs with features like appointment booking, health monitoring, medication reminders, and daily health reporting. Powered by the Qwen Code Instruct 2.5 7B model, the Planner and Caller Agents achieve an average RougeL score of 85.5 for planning and 96.5 for calling for our tasks while being lightweight for on-device deployment. This innovative approach combines the benefits of ondevice systems with multi-agent architectures, paving the way for user-centric healthcare solutions.
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