Evidence map›Paper›PMID 41585134›Full record

ReviewJournal of dental sciences2026

Applications of artificial intelligence in tooth extraction: A systematic review.

Masakazu Hamada, Ryota Nomura, Tatsuya Akitomo, Satoru Kusaka, Yuko Iwamoto, Shiori Yamamoto, Yuko Ogaya, Kazuhiko Nakano

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of dental sciences, 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

8 authors.

Masakazu HamadaDepartment of Oral & Maxillofacial Oncology and Surgery, Graduate School of Dentistry, The University of Osaka, Suita, Osaka, Japan.
Ryota NomuraDepartment of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Tatsuya AkitomoDepartment of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Satoru KusakaDepartment of Pediatric Dentistry, Hiroshima University Hospital, Hiroshima, Japan.
Yuko IwamotoDepartment of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Shiori YamamotoDepartment of Oral & Maxillofacial Oncology and Surgery, Graduate School of Dentistry, The University of Osaka, Suita, Osaka, Japan.
Yuko OgayaDepartment of Pediatric Dentistry, Graduate School of Dentistry, The University of Osaka, Suita, Osaka, Japan.
Kazuhiko NakanoDepartment of Pediatric Dentistry, Graduate School of Dentistry, The University of Osaka, Suita, Osaka, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background/purpose: Tooth extraction is a common procedure in dental treatment. In recent years, with the advancement of artificial intelligence (AI) technology, research on tooth extraction using AI has been increasing. In the present study, we consider the applicability of AI to tooth extraction through a literature review. Materials and methods: The PubMed, Scopus, and Web of Science databases were searched for ("tooth extraction") AND ("artificial intelligence" OR "machine learning" OR "deep learning") in June 2024. Results: Thirty-five articles matched the eligibility criteria and were extracted for this review. The most widely covered topics were "relationship between the root of the tooth and the inferior alveolar nerve" and "tooth extraction decision-making" with 10 and 8 articles, respectively. These two topics are considered to be important factors that determine risk and treatment options in clinical decision-making. Next, there were six articles about tooth extraction difficulty, preparation, and time, and four articles about maxillary sinus evaluation. Furthermore, there were three articles about predictive models for osteonecrosis and osteomyelitis of the jaw, and two articles each about post-extraction complications and the use of ChatGPT, which were the fewest in number. Conclusion: Findings from these papers will contribute to improving decision-making processes, treatment strategies, and preventive measures in dental care and are expected to serve as a foundation for future research. Furthermore, the diversity of each topic reflects the complexity and evolution of dental care and suggests that further exploration is warranted in future research.

Indexed as

Artificial intelligenceDeep learningMachine learningOral surgeryReviewTooth extraction

Identifiers

PMID41585134
PMCPMC12826025

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.