基于多智能体的边缘设备医疗助手

摘要

大型行动模型（LAMs）虽已革新智能自动化领域，但其在医疗健康中的应用却因隐私顾虑、延迟问题及对互联网连接的依赖而面临挑战。本报告介绍了一款设备端多代理医疗助手，有效克服了这些局限。该系统采用小型化、任务专精的代理，以优化资源利用，确保可扩展性与高性能。我们提出的系统集成了预约挂号、健康监测、用药提醒及日常健康报告等功能，成为一站式医疗解决方案。依托Qwen Code Instruct 2.5 7B模型，规划与呼叫代理在任务执行中分别实现了平均85.5和96.5的RougeL评分，同时保持轻量化，便于设备端部署。这一创新方法融合了设备端系统与多代理架构的优势，为以用户为中心的医疗解决方案开辟了新路径。

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.

基于多智能体的边缘设备医疗助手

Multi Agent based Medical Assistant for Edge Devices

摘要

Summary

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