本地医学大模型通过检索增强生成，准确率更高且更省电。
Accurate and Energy Efficient: Local Retrieval-Augmented Generation Models Outperform Commercial Large Language Models in Medical Tasks
- 用开源模型构建可监控能耗的检索增强生成框架
- 基于llama3.1:8b的RAG准确率达58.5%，能效比超商用模型2.7倍
- 适合关注医疗AI隐私、环保及低资源部署的研究者与机构
人工智能在医疗领域的应用日益广泛，但商业大模型如ChatGPT和DeepSeek对资源消耗巨大，且涉及患者隐私与安全风险。本文开发了一种可定制的医学领域检索增强生成（RAG）框架，实时监测能源使用与碳排放。基于多种开源LLM（包括通用模型llama3.1:8b和医疗专用模型medgemma-4b-it）构建RAG系统，并与DeepSeekV3-R1和OpenAI o4-mini进行对比评估。结果表明，基于llama3.1:8b的RAG模型准确率最高（58.5%），显著优于其他模型；其每千瓦时性能达0.52，总碳排放仅473克，电力消耗比o4-mini低172%，单位能耗准确率提升2.7倍。研究证明，本地部署的RAG模型在医疗任务中可实现更高精度与更低环境影响，支持可持续人工智能发展。
原文摘要 · Abstract (English)
Background The increasing adoption of Artificial Intelligence (AI) in healthcare has sparked growing concerns about its environmental and ethical implications. Commercial Large Language Models (LLMs), such as ChatGPT and DeepSeek, require substantial resources, while the utilization of these systems for medical purposes raises critical issues regarding patient privacy and safety. Methods We developed a customizable Retrieval-Augmented Generation (RAG) framework for medical tasks, which monitors its energy usage and CO2 emissions. This system was then used to create RAGs based on various open-source LLMs. The tested models included both general purpose models like llama3.1:8b and medgemma-4b-it, which is medical-domain specific. The best RAGs performance and energy consumption was compared to DeepSeekV3-R1 and OpenAIs o4-mini model. A dataset of medical questions was used for the evaluation. Results Custom RAG models outperformed commercial models in accuracy and energy consumption. The RAG model built on llama3.1:8B achieved the highest accuracy (58.5%) and was significantly better than other models, including o4-mini and DeepSeekV3-R1. The llama3.1-RAG also exhibited the lowest energy consumption and CO2 footprint among all models, with a Performance per kWh of 0.52 and a total CO2 emission of 473g. Compared to o4-mini, the llama3.1-RAG achieved 2.7x times more accuracy points per kWh and 172% less electricity usage while maintaining higher accuracy. Conclusion Our study demonstrates that local LLMs can be leveraged to develop RAGs that outperform commercial, online LLMs in medical tasks, while having a smaller environmental impact. Our modular framework promotes sustainable AI development, reducing electricity usage and aligning with the UNs Sustainable Development Goals.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。