ReviewFrontiers in dental medicine2025
Retrieval augmented generation in dentistry: potentials, applications, and future directions.
Review in Frontiers in dental medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Large language models answering structured questions on external cervical resorption: a comparative study.Odontology · 2026Article
- JADE-Plus: A Multimodal Agentic Retrieval-Augmented Generation Large Language Framework for Diagnostic Support in Jawbone Lesions: Development and Technical Validation Study.Journal of imaging informatics in medicine · 2026Article
- JADE: jawbone lesion diagnosis and decision supporting system.Dento maxillo facial radiology · 2026Article
- AI-enabled language technologies for language-mediated learning and clinical communication in international undergraduate dental education: a scoping review.Frontiers in medicine · 2026Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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with the generative capabilities of large language models, RAG addresses key limitations of standalone LLMs, including outdated parametric knowledge, limited domain specificity, and hallucinations.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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.