OsteoRAG: Retrieval-augmented generation for evidence-based osteoporosis knowledge management
Abstract
The exponential growth of biomedical literature poses significant challenges for knowledge management in specialized domains such as osteoporosis. While Large Language Models (LLMs) offer advanced natural language understanding capabilities, their direct application in high-risk medical contexts is hindered by hallucination risk, static knowledge, and limited traceability. This paper proposes a domain-specific Retrieval-Augmented Generation (RAG) system tailored for osteoporosis knowledge management, integrating authoritative sources from the International Osteoporosis Foundation and China's National Health Commission. The implemented system uses FAISS indexing with all-MiniLM-L6-v2 sentence embeddings and compares three retrieval strategies--Classic RAG, a fixed-budget non-agentic Multi-Query RAG (MQ-RAG), and Agentic RAG--against an LLM-only baseline. We evaluate the four conditions using a curated test set of 210 questions across true/false, single-choice, and open-ended formats. Results from three representative LLMs--Deepseek-v3.2, ChatGPT-5, and Qwen3-max--are reported for objective accuracy and expert-rated answer quality. Inter-rater agreement for the open-ended expert evaluation was substantial (weighted Fleiss's kappa = 0.72). This work highlights the potential of agentic RAG architectures for specialized medical question-answering systems.
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