用智能代理+检索生成，让医生快速查疫苗指南
AI-VaxGuide: An Agentic RAG-Based LLM for Vaccination Decisions
- 基于检索增强生成与智能代理框架，动态解析复杂问题
- 多步推理任务准确率显著优于传统方法，支持模糊提问
- 已集成到手机应用，适合临床实时查询疫苗信息
疫苗接种对全球公共卫生至关重要，但医护人员在紧急情况下常难以快速获取免疫接种指南。各国规程和世卫组织建议通常内容冗长复杂，难以精准提取关键信息。本项目开发了一个多语言智能问答系统，将静态疫苗指南转化为交互式知识库。系统基于检索增强生成（RAG）框架，并引入智能代理（Agentic RAG）进行推理，能针对复杂医疗问题提供准确、上下文相关的回答。评估显示，该方法在处理多步骤或模糊问题时显著优于传统方法。为支持临床使用，系统已集成至移动端应用，实现点对点实时访问核心疫苗信息。AI-VaxGuide 模型已开源，可在 https://huggingface.co/VaxGuide 获取。
原文摘要 · Abstract (English)
Vaccination plays a vital role in global public health, yet healthcare professionals often struggle to access immunization guidelines quickly and efficiently. National protocols and WHO recommendations are typically extensive and complex, making it difficult to extract precise information, especially during urgent situations. This project tackles that issue by developing a multilingual, intelligent question-answering system that transforms static vaccination guidelines into an interactive and user-friendly knowledge base. Built on a Retrieval-Augmented Generation (RAG) framework and enhanced with agent-based reasoning (Agentic RAG), the system provides accurate, context-sensitive answers to complex medical queries. Evaluation shows that Agentic RAG outperforms traditional methods, particularly in addressing multi-step or ambiguous questions. To support clinical use, the system is integrated into a mobile application designed for real-time, point-of-care access to essential vaccine information. AI-VaxGuide model is publicly available on https://huggingface.co/VaxGuide
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