# Enhancing Health Information Retrieval with RAG by Prioritizing Topical Relevance and Factual Accuracy

arXiv: https://arxiv.org/abs/2502.04666 · HTML: https://mlsanity.com/paper/2502.04666/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2025.

## Built on by (most cited first, 6 of 6)

- [ModernBERT + ColBERT: Enhancing biomedical RAG through an advanced re-ranking retriever](https://mlsanity.com/paper/2510.04757/) (2025)
- [Fine-Tuning MedGemma for Clinical Captioning to Enhance Multimodal RAG over Malaysia CPGs](https://mlsanity.com/paper/2510.15418/) (2025)
- [Contradictions in Context: Challenges for Retrieval-Augmented Generation in Healthcare](https://mlsanity.com/paper/2511.06668/) (2025)
- [HySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysis](https://mlsanity.com/paper/2508.05666/) (2025)
- [Interpretability Framework for LLMs in Undergraduate Calculus](https://mlsanity.com/paper/2510.17910/) (2025)
- [Enhancing LLMs with Context-Specific Knowledge for Mitigating Misinformation in SMEs: A RAG-based Modeling and Analysis](https://mlsanity.com/paper/2608.00006/) (2026)

## Abstract (arXiv)

The exponential surge in online health information, coupled with its increasing use by non-experts, highlights the pressing need for advanced Health Information Retrieval models that consider not only topical relevance but also the factual accuracy of the retrieved information, given the potential risks associated with health misinformation. To this aim, this paper introduces a solution driven by Retrieval-Augmented Generation (RAG), which leverages the capabilities of generative Large Language Models (LLMs) to enhance the retrieval of health-related documents grounded in scientific evidence. In particular, we propose a three-stage model: in the first stage, the user's query is employed to retrieve topically relevant passages with associated references from a knowledge base constituted by scientific literature. In the second stage, these passages, alongside the initial query, are processed by LLMs to generate a contextually relevant rich text (GenText). In the last stage, the documents to be retrieved are evaluated and ranked both from the point of view of topical relevance and factual accuracy by means of their comparison with GenText, either through stance detection or semantic similarity. In addition to calculating factual accuracy, GenText can offer a layer of explainability for it, aiding users in understanding the reasoning behind the retrieval. Experimental evaluation of our model on benchmark datasets and against baseline models demonstrates its effectiveness in enhancing the retrieval of both topically relevant and factually accurate health information, thus presenting a significant step forward in the health misinformation mitigation problem.