Evidence map›Paper›PMID 39748267›Full record

ArticleBMC oral health2025

Can ChatGPT be guide in pediatric dentistry?

Canan Bayraktar Nahir

Abstract read
In one paragraph

Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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  14. Can Artificial Intelligence Language Models Effectively Address Dental Trauma Questions?Dental traumatology : official publication of International Association for Dental Traumatology · 2025
    Observational
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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

1 author.

Canan Bayraktar NahirDepartment of Pediatric Dentistry, Faculty of Dentistry, Tokat Gaziosmanpaşa University, Tokat, Türkiye. canannbayraktarr@gmail.com.ORCID 0000-0003-1388-1778

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe use of ChatGPT in the field of health has recently gained popularity. In the field of dentistry, ChatGPT can provide services in areas such as, dental education and patient education. The aim of this study was to evaluate the quality, readability and originality of pediatric patient/parent information and academic content produced by ChatGPT in the field of pediatric dentistry.

methodsA total of 60 questions were asked to ChatGPT for each topic (dental trauma, fluoride, and tooth eruption/oral health) consisting of pediatric patient/parent questions and academic questions. The modified Global Quality Scale (the scoring ranges from 1: poor quality to 5: excellent quality) was used to evaluate the quality of the answers and Flesch Reading Ease and Flesch-Kincaid Grade Level were used to evaluate the readability. A similarity index was used to compare the quantitative similarity of the answers given by the software with the guidelines and academic references in different databases.

resultsThe evaluation of answers quality revealed an average score of 4.3 ± 0.7 for pediatric patient/parent questions and 3.7 ± 0.8 for academic questions, indicating a statistically significant difference (p < 0.05). Academic questions regarding dental trauma received the lowest scores (p < 0.05). However, no significant differences were observed in readability and similarity between ChatGPT answers for different question groups and topics (p > 0.05).

conclusionsIn pediatric dentistry, ChatGPT provides quality information to patients/parents. ChatGPT, which is difficult to readability for patients/parents and offers an acceptable similarity rate, needs to be improved in order to interact with people more efficiently and fluently.

Indexed as

Pediatric DentistryChildComprehensionHealth Education, DentalHumansInternetParentsPatient Education as TopicTooth EruptionTooth InjuriesArtificial intelligenceFluoridesOral healthPediatric dentistryPublic health informaticsTooth eruptionTooth injuries

Identifiers

PMID39748267
PMCPMC11697937

What OpenQuestion holds

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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.