AI-VaxGuide: An Agentic RAG-Based LLM for Vaccination Decisions
On the map: LLM agents and tool use → Medical and scientific domain agents
Abstract (arXiv)
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
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 8 of its 28 references that are in the map. First posted on arXiv 4 Jul 2025.
Builds on
8 of 28 references.
References · 8
- 2025Can A Society of Generative Agents Simulate Human Behavior and Inform Public Health Policy? A Case Study on Vaccine Hesitancycited by 15
- 2023AE-GPT: Using Large Language Models to Extract Adverse Events from Surveillance Reports-A Use Case with Influenza Vaccine Adverse Eventscited by 8
- 2024Accuracy of a Large Language Model in Distinguishing Anti- And Pro-vaccination Messages on Social Media: The Case of Human Papillomavirus Vaccinationcited by 5
- 2025VaxGuard: A Multi-Generator, Multi-Type, and Multi-Role Dataset for Detecting LLM-Generated Vaccine Misinformationcited by 5
- 2024Optimizing Social Media Annotation of HPV Vaccine Skepticism and Misinformation Using Large Language Models: An Experimental Evaluation of In-Context Learning and Fine-Tuning Stance Detection Across Multiple Modelscited by 2
- 2025Large Language Model Chatbot Conversations vs Public Health Materials and Parental HPV Vaccination Intentions: A Randomized Clinical Trialcited by 2
- 2023Decoding Concerns: Multi-label Classification of Vaccine Sentiments in Social Mediacited by 1
- 2025Working with Large Language Models to Enhance Messaging Effectiveness for Vaccine Confidencecited by 1
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Citing papers per year: 2025: 1.
1 paper.
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- 2025Talk Before You Retrieve: Agent-Led Discussions for Better RAG in Medical QA
- 2025Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge
- 2025Demo: Guide-RAG: Evidence-Driven Corpus Curation for Retrieval-Augmented Generation in Long COVID
- 2026RAG-X: Systematic Diagnosis of Retrieval-Augmented Generation for Medical Question Answering
- 2026From Conflict to Consensus: Boosting Medical Reasoning via Multi-Round Agentic RAG
- 2026A corpus-specific clinical RAG system matches or outperforms newer frontier LLMs on HealthBench
- 2025FHIR-AgentBench: Benchmarking LLM Agents for Realistic Interoperable EHR Question Answering
- 2026SEMA-RAG: A Self-Evolving Multi-Agent Retrieval-Augmented Generation Framework for Medical Reasoning