Med-KAG: Preliminary Results of a Medical Knowledge-Augmented Generation Approach
Abstract
We propose in this paper Med-KAG, a novel Artificial Intelligence (AI) assistant architecture for clinical decision support. Med-KAG extends the Retrieval-Augmented Generation (RAG) paradigm by integrating a medical Knowledge Graph built from the Unified Medical Language System (UMLS) Metathesaurus. This approach grounds the model's responses in verified biomedical relationships between diseases, symptoms, and treatments, reducing hallucinations and improving transparency. Our preliminary evaluation on MedQA-US compares a baseline large language model (Qwen3-235B-A22B) with its knowledge-enhanced variant, showing comparable accuracy while identifying the retriever as the primary source of error. These results highlight both the potential and current limitations of combining structured medical knowledge with generative AI to achieve more reliable and explainable clinical assistance.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.