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 Systematic reviews, long the cornerstone of evidence-based dentistry, are resource-intensive and often outdated within a few years, widening the gap between current research and clinical practice.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 answers. For dental clinicians, this approach enables rapid, citation-linked answers to practice-relevant questions—such as material selection, healing outcomes, or procedural comparisons—without relying on outdated narrative summaries. We describe three complementary integration pathways—RAG on pre-retrieved article pools, public living review portals, and machine-actionable journal publications—each with distinct requirements and benefits. Looking forward, emerging agentic AI systems, capable of planning multi-step searches and iterative updates, may further enhance these capabilities. Although this framework is conceptually grounded and supported by emerging methodological evidence, prospective empirical validation, benchmarking against existing review approaches, and real-world deployment studies will be required to fully assess its performance, reliability, and impact on clinical decision-making. Conclusion RAG offers a scalable, transparent alternative to static systematic reviews and can shorten the research-to-practice timeline. By automating retrieval and initial synthesis while keeping human critical appraisal and ethical judgment central, it points toward an era of augmented rather than automated intelligence in evidence-based dentistry.
From reviews to real-time: dynamic evidence in dentistry / Gavrilova, A. V.; Galli, C.. - In: EVIDENCE-BASED DENTISTRY. - ISSN 1462-0049. - (2026). [10.1038/s41432-026-01206-2]
From reviews to real-time: dynamic evidence in dentistry
Galli, C.
Writing – Review & Editing
2026-01-01
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 answers. For dental clinicians, this approach enables rapid, citation-linked answers to practice-relevant questions—such as material selection, healing outcomes, or procedural comparisons—without relying on outdated narrative summaries. We describe three complementary integration pathways—RAG on pre-retrieved article pools, public living review portals, and machine-actionable journal publications—each with distinct requirements and benefits. Looking forward, emerging agentic AI systems, capable of planning multi-step searches and iterative updates, may further enhance these capabilities. Although this framework is conceptually grounded and supported by emerging methodological evidence, prospective empirical validation, benchmarking against existing review approaches, and real-world deployment studies will be required to fully assess its performance, reliability, and impact on clinical decision-making. Conclusion RAG offers a scalable, transparent alternative to static systematic reviews and can shorten the research-to-practice timeline. By automating retrieval and initial synthesis while keeping human critical appraisal and ethical judgment central, it points toward an era of augmented rather than automated intelligence in evidence-based dentistry.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
