Evidence map›Paper›PMID 42092910›Full record

ArticleBMC oral health2026

Are chatbots reliable sources of information regarding fluoride in pediatric dentistry?

Dilara Dinc, Simin Kocaaydin, Sabiha Ceren Ilisulu

Abstract read
In one paragraph

Article in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

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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

3 authors.

Dilara DincFaculty of Dentistry, Department of Pediatric Dentistry, Biruni University, 10. Yıl Street, No: 45 Topkapı, Zeytinburnu, Istanbul, 34010, Turkey. dr.dt.dilaradinc@gmail.com.ORCID 0009-0002-2940-3762
Simin KocaaydinFaculty of Dentistry, Department of Pediatric Dentistry, Kent University, Istanbul, Turkey.ORCID 0000-0003-2577-7513
Sabiha Ceren IlisuluFaculty of Dentistry, Department of Pediatric Dentistry, Istanbul University-Cerrahpaşa, Istanbul, Turkey.ORCID 0000-0003-3679-4001

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo evaluate the accuracy and consistency of responses generated by artificial intelligence (AI) chatbots in pediatric dentistry, specifically concerning fluoride usage. STUDY

designDescriptive cross-sectional study.

methodsFour AI chatbots (ChatGPT, Gemini, Claude, Copilot) and four groups of dental professionals (pediatric dentists, general dentists, pediatric dentistry PhD students, and fifth-year dental students) answered 23 true-false questions based on IAPD, AAPD and EAPD guidelines. Each chatbot was tested 28 times per question in separate sessions. Accuracy was analyzed across four categories: Individual Topical Fluoride Applications, Professional Topical Fluoride Applications, Systemic Fluoride Applications, and Fluorosis. All groups were statistically compared with each other to evaluate differences in response accuracy across AI chatbots and human participant categories.

resultsSignificant differences were observed in the accuracy of chatbot responses across fluoride application categories (p < 0.05). Claude achieved perfect accuracy in Systemic Fluoride Applications (100%), while the other AI models performed lower-with ChatGPT scoring the lowest (94.3%)-and Gemini showed the highest accuracy in Fluorosis-related questions (76.8%). Among professionals, pediatric dentists (82.3%) consistently had the highest accuracy. STATISTICS: Chi-square and Fisher's Exact tests were used to assess differences in response accuracy between groups. A p-value < 0.05 was considered statistically significant.

conclusionsClaude and Gemini demonstrated greater reliability in fluoride-related questions than ChatGPT and Copilot. However, expert oversight remains crucial in pediatric dental care.

Indexed as

Artificial IntelligenceCariostatic AgentsFluoridesPediatric DentistryCross-Sectional StudiesFluorides, TopicalFluoride TreatmentFluorosis, DentalHumansStudents, DentalCariostatic AgentsFluoridesFluorides, TopicalAccuracyArtificial intelligenceChatbotsFluoridePediatric dentistry

Identifiers

PMID42092910
PMCPMC13339393

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.