  Large language models (LLMs) are revolutionizing various domains with their
remarkable natural language processing (NLP) abilities. However, deploying LLMs
in resource-constrained edge computing and embedded systems presents
significant challenges. Another challenge lies in delivering medical assistance
in remote areas with limited healthcare facilities and infrastructure. To
address this, we introduce MedAide, an on-premise healthcare chatbot. It
leverages tiny-LLMs integrated with LangChain, providing efficient edge-based
preliminary medical diagnostics and support. MedAide employs model
optimizations for minimal memory footprint and latency on embedded edge devices
without server infrastructure. The training process is optimized using low-rank
adaptation (LoRA). Additionally, the model is trained on diverse medical
datasets, employing reinforcement learning from human feedback (RLHF) to
enhance its domain-specific capabilities. The system is implemented on various
consumer GPUs and Nvidia Jetson development board. MedAide achieves 77\%
accuracy in medical consultations and scores 56 in USMLE benchmark, enabling an
energy-efficient healthcare assistance platform that alleviates privacy
concerns due to edge-based deployment, thereby empowering the community.
