Multi Agent based Medical Assistant for Edge Devices
This provides a user-centric healthcare solution for edge devices, though it is incremental as it adapts existing multi-agent architectures to a specific domain.
The paper tackles the challenges of applying Large Action Models in healthcare by introducing an on-device, multi-agent assistant that addresses privacy, latency, and internet dependency issues, achieving average RougeL scores of 85.5 for planning and 96.5 for calling tasks.
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.