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.

通过整合LangChain，MedAide利用嵌入式边缘设备上的小型语言模型进行初步医疗诊断和支持，通过低秩适应优化训练过程，并利用强化学习从人类反馈中增强其领域特定能力，从而实现了一个能够解决资源受限边缘计算和嵌入式系统中部署LLMs的医疗辅助平台，提供高效的医疗援助，减轻边缘部署带来的隐私问题，从而为社区提供动力。

MedAide：利用大型语言模型为边缘设备提供本地医疗援助