基於多代理系統的邊緣設備醫療助手

Multi Agent based Medical Assistant for Edge Devices

March 7, 2025

作者: Sakharam Gawade, Shivam Akhouri, Chinmay Kulkarni, Jagdish Samant, Pragya Sahu, Aastik, Jai Pahal, Saswat Meher

cs.AI

摘要

大型行動模型（LAMs）已徹底革新了智能自動化領域，但其在醫療保健中的應用卻因隱私問題、延遲及對網絡連接的依賴而面臨挑戰。本報告介紹了一種設備端的多代理醫療助手，成功克服了這些限制。該系統利用小型、任務專用的代理來優化資源，確保可擴展性和高性能。我們提出的系統作為一站式醫療保健解決方案，具備預約掛號、健康監測、用藥提醒及日常健康報告等功能。基於Qwen Code Instruct 2.5 7B模型，規劃者與呼叫者代理在我們的任務中分別達到了平均85.5和96.5的RougeL分數，同時保持輕量化，適合設備端部署。這一創新方法結合了設備端系統與多代理架構的優勢，為以用戶為中心的醫療保健解決方案開闢了新路徑。

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.