Multi Agent based Medical Assistant for Edge Devices
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.
67d2b618919e3b981ada89ce
Sakharam Gawade
claimed_verified
2025-03-14T08:58:34.521Z
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Sakharam Gawade
user
sakharamg
67d2b618919e3b981ada89cf
Shivam Akhouri
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Chinmay Kulkarni
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Jagdish Samant
67d2b618919e3b981ada89d2
Pragya Sahu
67d2b618919e3b981ada89d3
Aastik
67d2b618919e3b981ada89d4
Jai Pahal
67d2b618919e3b981ada89d5
Saswat Meher
Multi Agent based Medical Assistant for Edge Devices
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.
An on-device multi-agent healthcare assistant powered by the Qwen Code Instruct 2.5 7B model provides efficient healthcare services with features like appointment booking, health monitoring, and medication reminders, overcoming privacy and latency issues.
Qwen Code Instruct 2.5 7B RougeL score multi-agent architecture