MedAide: Leveraging Large Language Models for On-Premise Medical Assistance on Edge Devices

แนวคิดหลัก

LLMs are revolutionizing healthcare with MedAide, providing efficient medical assistance on edge devices.

บทคัดย่อ

MedAide introduces an on-premise healthcare chatbot leveraging tiny-LLMs integrated with LangChain for preliminary medical diagnostics. The system optimizes model training using low-rank adaptation and reinforcement learning from human feedback. Implemented on consumer GPUs and Nvidia Jetson, MedAide achieves 77% accuracy in medical consultations. It addresses challenges of deploying LLMs on resource-constrained devices while minimizing latency. The system offers a vital solution for improving preliminary diagnosis in remote areas with limited healthcare facilities. By training models on diverse medical datasets, MedAide empowers an energy-efficient healthcare assistance platform that alleviates privacy concerns due to edge-based deployment.

สถิติ

MedAide achieves 77% accuracy in medical consultations.
Scores 56 in USMLE benchmark.

คำพูด

"Large language models (LLMs) are revolutionizing various domains with their remarkable natural language processing (NLP) abilities."
"MedAide offers a vital on-premise healthcare solution, significantly improving preliminary diagnosis in remote areas."
"Our proposed MedAide system leverages LLMs with optimizations, enabling seamless deployment on devices such as Nvidia Jetson or consumer-grade GPUs."

ข้อมูลเชิงลึกที่สำคัญจาก

MedAide

by Abdul Basit,... ที่ arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.00830.pdf

สอบถามเพิ่มเติม

How can the integration of LangChain enhance the efficiency of medical database searches

LangChain integration enhances the efficiency of medical database searches by structuring interactions with the model in a way that mitigates hallucinations. By segmenting extensive medical knowledge into manageable blocks and generating embeddings for each block, LangChain streamlines the search process. This approach allows for faster retrieval of relevant information from medical databases, enabling MedAide to provide accurate and reliable healthcare support. Additionally, by utilizing GPU-accelerated tools like FAISS for similarity search, LangChain significantly boosts search speeds, making the entire process more efficient and effective.

What are the implications of relying heavily on server-based deployment for large language models

Relying heavily on server-based deployment for large language models can have several implications. Firstly, it limits accessibility as users may require constant internet connectivity to access these models, restricting their usage in remote or resource-constrained areas. Moreover, server-based deployment often leads to increased power consumption and computational resources, making it less energy-efficient compared to edge-based solutions like MedAide. Additionally, server dependency can raise privacy concerns due to data being processed externally rather than on local devices.

How can the use of reinforcement learning from human feedback impact the performance of LLMs like MedAide

The use of reinforcement learning from human feedback (RLHF) can significantly impact the performance of LLMs like MedAide by enhancing their domain-specific understanding and refining their responses based on practical insights. By training a reward model through RLHF and integrating it into the training cycle using Proximal Policy Optimization (PPO), MedAide can adapt its learning strategy dynamically based on human feedback. This iterative process ensures that the model's outputs not only maintain technical accuracy but also align closely with real-world applicability and user expectations. As a result, MedAide becomes more robust and contextually aware in addressing nuanced demands within medical applications with greater precision.