Retrieval augmented generation in dentistry: potentials, applications, and future directions
Abstract
Retrieval Augmented Generation offers a robust framework for developing reliable and evidence- aligned artificial intelligence in dentistry. By integrating external knowledge sources with the generative capabilities of large language models, RAG addresses key limitations of standalone LLMs, including outdated parametric knowledge, limited domain specificity, and hallucinations. This mini-review outlines the foundations of RAG, its core architectural components, and early dental applications that demonstrate improved factual accuracy and interpretability. Despite promising progress, research remains preliminary, constrained by heterogeneous methods and limited dental corpora. Advancing RAG will require standardized evaluation frameworks, curated knowledge bases, and multimodal resources to support trustworthy AI in dental research and practice.
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