ReviewBDJ open2026
Current status, evidence maturity, and translational readiness of artificial intelligence in dentistry: a bibliometric and thematic analysis.
Review in BDJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) has rapidly expanded across dental research, particularly in imaging-based diagnostics and digital workflows. Despite the accelerating publication growth, the structural evolution, thematic maturity, and translational readiness of AI in dentistry remain insufficiently synthesised.
methodsPublications related to AI in dentistry between 2015 and 2025 were retrieved from the Scopus database. Bibliometric analyses were conducted to examine annual outputs, countries, institutions, authors, journals, and keyword co-occurrence patterns. Network visualisation and clustering were performed using VOSviewer to identify research hotspots and thematic evolution. Selected evidence syntheses were evaluated using the Risk of Bias in Systematic Reviews (ROBIS) tool to assess methodological robustness and evidence maturity.
resultsA total of 3665 publications were included. Research output demonstrated marked post-2019 acceleration, with deep learning and convolutional neural networks dominating the methodological landscape. The United States, China, and India were the most productive countries, while a core group of highly connected authors shaped collaborative structures. Keyword clustering revealed a clear thematic progression from algorithmic development and image segmentation toward diagnostic imaging applications, digital dentistry integration, and workflow optimisation. Among the 75 systematic reviews evaluated using ROBIS, 50 (66.7%) demonstrated an unclear overall risk of bias, 24 (32.0%) showed a low risk of bias, and only one (1.3%) was classified as high risk, indicating that methodological transparency and evidence synthesis have not progressed at the same pace as technological innovation.
conclusionAI research in dentistry is experiencing rapid expansion and increasing international collaboration, and continuous thematic evolution towards clinically relevant applications. However, the ROBIS assessment indicates that improvements in methodological quality and reporting transparency of evidence syntheses are still required to strengthen confidence in the current evidence base. Future studies should prioritise multicentre validation, standardised evaluation frameworks, rigorous systematic reviews, and clinically meaningful outcome measures to facilitate the safe and effective translation of AI into routine dental practice.
Identifiers
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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.