CliniQ_RAG: A Hybrid Retrieval-Augmented Generation System for the Medical Field

CliniQ_RAG is a hybrid retrieval-augmented generation system specifically designed for the medical field. It combines BM25, semantic search, and re-ranking technologies to address the "hallucination" problem in AI medical Q&A, providing accurate and interpretable answers that support multiple scenarios such as clinical decision-making and medical education.

Traditional language models are prone to "hallucinations" (generating seemingly reasonable but inaccurate information) in medical Q&A; Retrieval-Augmented Generation (RAG) technology provides a new approach to solving this problem; CliniQ_RAG was born in this context, combining multiple retrieval technologies and re-ranking mechanisms to aim for more accurate and reliable answers to medical Q&A questions.

The core innovation of CliniQ_RAG is its three-layer retrieval architecture:

1. BM25 Algorithm: Based on statistical features such as term frequency and document length, it quickly locates documents containing specific medical terms;
2. Semantic Search: Captures the semantic similarity between queries and documents through vector embeddings, understanding synonyms and related expressions of medical concepts;
3. Re-ranking Mechanism: Performs refined relevance evaluation on candidate documents from the first two layers to improve result quality.

CliniQ_RAG adopts a modular design with clear component interfaces, ensuring strong maintainability and scalability; Data preprocessing supports multiple medical literature formats such as PDF, HTML, and plain text to extract structured knowledge; Model selection leverages the open-source ecosystem, supporting multiple pre-trained models as generation backends, and the retrieval and re-ranking modules use validated open-source implementations.

CliniQ_RAG has a wide range of application scenarios:

• Clinical Practice: As an intelligent assistant for doctors, it helps retrieve clinical guidelines and case reports to provide references for clinical decision-making;
• Medical Research: Accelerates the writing of literature reviews and improves research efficiency;
• Medical Education: Helps students understand medical knowledge and cultivate critical thinking;
• It can also be applied in fields such as drug development and public health monitoring.

Limitations of CliniQ_RAG:

1. Performance depends on the quality and coverage of the retrieval library; if literature is not included, accurate answers cannot be provided;
2. Medical knowledge updates rapidly, so maintaining the timeliness of the retrieval library is an ongoing challenge. Future Directions: Explore multi-modal retrieval (integrating medical images and genomic data), combine knowledge graphs to build structured medical knowledge representations, and enhance reasoning capabilities and answer quality.

CliniQ_RAG represents an important exploration direction in the field of medical AI, solving the "hallucination" problem by combining RAG technology with medical expertise; as technology evolves and data resources become richer, such systems will play an increasingly important role in the healthcare field.