Evidence map›Paper›PMID 42015461›Full record

ReviewAustralian endodontic journal : the journal of the Australian Society of Endodontology Inc2026

Artificial Intelligence in Endodontics-A Bibliometric Analysis.

Eric Chen, Justin Jun-Young Eun, Alan Chen, Sepanta Hosseinpour, Hossein Mohammad-Rahimi, Ove Andreas Peters, Philip Yuan-Ho Chien

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Australian endodontic journal : the journal of the Australian Society of Endodontology Inc, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Eric ChenSchool of Dentistry, The University of Queensland, Herston, Queensland, Australia.
Justin Jun-Young EunSchool of Dentistry, The University of Queensland, Herston, Queensland, Australia.
Alan ChenSchool of Dentistry, The University of Queensland, Herston, Queensland, Australia.
Sepanta HosseinpourSchool of Dentistry, The University of Queensland, Herston, Queensland, Australia.
Hossein Mohammad-RahimiDepartment of Dentistry and Oral Health, Aarhus University, Aarhus, Denmark.
Ove Andreas PetersSchool of Dentistry, The University of Queensland, Herston, Queensland, Australia.ORCID https://orcid.org/0000-0001-5222-8718
Philip Yuan-Ho ChienSchool of Dentistry, The University of Queensland, Herston, Queensland, Australia.ORCID https://orcid.org/0000-0002-8902-0272

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to map and evaluate research trends in artificial intelligence applied to endodontics using bibliometric methods. A comprehensive literature search was conducted in PubMed, EMBASE, and Scopus in April 2025 to identify studies addressing artificial intelligence in endodontic diagnosis, treatment, and education. Titles, abstracts, and full texts were screened according to predefined criteria. Bibliometric indicators including publication output, authorship networks, institutional and country contributions, citation patterns, and keyword co-occurrence were analysed using bibliometric software. 220 studies were included. Research output increased markedly over time, with major contributions from China, the United States, Turkey and India. Collaboration was largely geographically clustered, with limited international integration. Recent publications showed a strong focus on deep learning approaches for diagnostic imaging, particularly cone-beam computed tomography and image segmentation. Artificial intelligence research in endodontics is expanding rapidly, but broader collaboration and diversification beyond radiographic applications are needed to support clinical translation.

Indexed as

Artificial IntelligenceBibliometricsEndodonticsCone-Beam Computed TomographyHumansartificial intelligencebibliometricsdeep learningendodontics

Identifiers

PMID42015461
PMCPMC13436042

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.