Evidence map›Paper›PMID 41610482›Full record

ArticleInternational dental journal2026

Generative Artificial Intelligence and Large Language Models in Paediatric Dentistry: A Scoping Review.

Tatsuya Akitomo, Masakazu Hamada, Shuma Hamaguchi, Satoru Kusaka, Ryota Nomura

Abstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Tatsuya AkitomoDepartment of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan. Electronic address: takitomo@hiroshima-u.ac.jp.
Masakazu HamadaDepartment of Oral & Maxillofacial Oncology and Surgery, Graduate School of Dentistry, The University of Osaka, Suita, Osaka, Japan.
Shuma HamaguchiDepartment of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
Satoru KusakaDepartment of Pediatric Dentistry, Hiroshima University Hospital, Hiroshima, Japan.
Ryota NomuraDepartment of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Large language models (LLMs), a type of artificial intelligence (AI), have been applied to various fields of dentistry in recent years. Although several reviews have examined AI applications in paediatric dentistry, most have focused on diagnostic assistance, and none have comprehensively addressed LLM use, including not only dental professionals but also patients or their guardians. PubMed, Scopus, and Web of Science were searched in September 2025 using the terms "Pediatric dentistry" AND "ChatGPT" OR "Gemini" OR "Claude" OR "Copilot" OR "DeepSeek." Of the 262 articles identified, 30 met the eligibility criteria and were included in this review-24 published in 2025 and 6 in 2024. The largest group of studies (18 articles) focused on "answers to questions," followed by 8 on "diagnostic assistance" and 3 on "dental examination." LLMs demonstrated promising potential in paediatric dentistry across a range of users, including dental students, professionals, patients, and guardians. However, because LLMs can still provide inaccurate or misleading information, they should currently be used only as a supplementary tool. Recognizing these limitations and applying LLMs appropriately is essential. Continued research may further expand the scope and reliability of LLM applications in paediatric dentistry.

Indexed as

Artificial IntelligenceGenerative Artificial IntelligenceLarge Language ModelsPediatric DentistryChildHumansArtificial intelligenceDiagnostic assistanceLarge language modelsPaediatric dentistryPatient education

Identifiers

PMID41610482
PMCPMC12873718

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.