title: Multi Agent based Medical Assistant for Edge Devices
summary: 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.
paper_authors._id: 67d2b618919e3b981ada89ce
paper_authors.name: Sakharam Gawade
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-14T08:58:34.521Z
paper_authors._id: 62eb62073f7c3dd05faa5e1e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62eb62073f7c3dd05faa5e1e/NsmeB-Ps1ZPNh6T0HEsTo.jpeg
paper_authors.fullname: Sakharam Gawade
paper_authors.type: user
paper_authors.user: sakharamg
paper_authors._id: 67d2b618919e3b981ada89cf
paper_authors.name: Shivam Akhouri
paper_authors._id: 67d2b618919e3b981ada89d0
paper_authors.name: Chinmay Kulkarni
paper_authors._id: 67d2b618919e3b981ada89d1
paper_authors.name: Jagdish Samant
paper_authors._id: 67d2b618919e3b981ada89d2
paper_authors.name: Pragya Sahu
paper_authors._id: 67d2b618919e3b981ada89d3
paper_authors.name: Aastik
paper_authors._id: 67d2b618919e3b981ada89d4
paper_authors.name: Jai Pahal
paper_authors._id: 67d2b618919e3b981ada89d5
paper_authors.name: Saswat Meher
paper_title: Multi Agent based Medical Assistant for Edge Devices
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Qwen Code Instruct 2.5 7B RougeL score multi-agent architecture