Biomedical Literature Q&A System Using Retrieval-Augmented Generation (RAG)

• URL: http://arxiv.org/abs/2509.05505v1
• Date: Fri, 05 Sep 2025 21:29:52 GMT
• Title: Biomedical Literature Q&A System Using Retrieval-Augmented Generation (RAG)
• Authors: Mansi Garg, Lee-Chi Wang, Bhavesh Ghanchi, Sanjana Dumpala, Shreyash Kakde, Yen Chih Chen,
• Abstract summary: This work presents a Biomedical Literature Question Answering (Q&A) system based on a Retrieval-Augmented Generation architecture.<n>The system integrates diverse sources, including PubMed articles, curated Q&A datasets, and medical encyclopedias.<n>The system supports both general medical queries and domain-specific tasks, with a focused evaluation on breast cancer literature.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work presents a Biomedical Literature Question Answering (Q&A) system based on a Retrieval-Augmented Generation (RAG) architecture, designed to improve access to accurate, evidence-based medical information. Addressing the shortcomings of conventional health search engines and the lag in public access to biomedical research, the system integrates diverse sources, including PubMed articles, curated Q&A datasets, and medical encyclopedias ,to retrieve relevant information and generate concise, context-aware responses. The retrieval pipeline uses MiniLM-based semantic embeddings and FAISS vector search, while answer generation is performed by a fine-tuned Mistral-7B-v0.3 language model optimized using QLoRA for efficient, low-resource training. The system supports both general medical queries and domain-specific tasks, with a focused evaluation on breast cancer literature demonstrating the value of domain-aligned retrieval. Empirical results, measured using BERTScore (F1), show substantial improvements in factual consistency and semantic relevance compared to baseline models. The findings underscore the potential of RAG-enhanced language models to bridge the gap between complex biomedical literature and accessible public health knowledge, paving the way for future work on multilingual adaptation, privacy-preserving inference, and personalized medical AI systems.

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