Abstract Background The exponential growth of biomedical literature—over a million new PubMed entries each year—has outpaced traditional evidence-synthesis methods. Systematic reviews, long the cornerstone of evidence-based dentistry, are resource-intensive and often outdated within a few years, widening the gap between current research and clinical practice. Methods We outline Retrieval-Augmented Generation (RAG) as a methodology for dynamic evidence reviews. RAG strengthens Large Language Models (LLMs) by combining their generative capacity with real-time retrieval from a continuously updated, curated knowledge base. This design grounds every answer in verifiable sources and mitigates the factual errors and hallucinations seen in standalone LLMs. Results/Implications RAG enables on-demand dynamic synthesis of the latest evidence, allowing clinicians and researchers to ask complex, natural-language questions and receive concise, fully cited answer
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