Evidence map›Paper›PMID 41299374›Full record

ArticleBMC oral health2025

Comparison of the information of generative artificial intelligence large language models and professional guidelines regarding nutritional advice for orthodontic patients.

Tevhide Sokmen, İrem Kar, Cumhur Tuncer, Burcu Baloş Tuncer

Abstract readComparative Study
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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

4 authors.

Tevhide SokmenDepartment of Orthodontics, Faculty of Dentistry, University of Gazi, Ankara, Turkey. tevhidesokmen@gazi.edu.tr.ORCID 0000-0003-1613-3193
İrem KarDepartment of Biostatistics, Faculty of Medicine, University of Ankara, Ankara, Turkey.ORCID 0000-0002-1747-4312
Cumhur TuncerDepartment of Orthodontics, Faculty of Dentistry, University of Gazi, Ankara, Turkey.ORCID 0000-0003-3275-5718
Burcu Baloş TuncerDepartment of Orthodontics, Faculty of Dentistry, University of Gazi, Ankara, Turkey.ORCID 0000-0001-7779-3692

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo evaluate the credibility of large language models (LLMs) compared to American Association of Orthodontists (AAO) and British Orthodontic Society (BOS) guides regarding nutritional guidelines for orthodontic patients.

methodsThe responses offered by ChatGPT 4.0, Copilot and Gemini were assessed for information credibility about tooth decay, food, beverages, oral care, and further assistance as; compatible, compatible but insufficient, partially incompatible and incompatible compared to the guidelines. Reliability was analyzed by the DISCERN tool. The readability of the sources was assessed using the Flesch Reading Ease Score and the Flesch-Kincaid Grade Level. The Friedman Test was conducted to compare DISCERN scores.

resultsResponses of LLMs were understandable and compatible with the guidelines, but detailed information on tooth decay, dental plaque and risks of acidic environments were inadequate. ChatGPT 4.0, Copilot and Gemini provided detailed lists of foods to avoid and include. Only AAO suggested being aware of extreme temperatures and usage of sugar-free gums. All sources mentioned the necessity of good oral hygiene, but oral hygiene tools were not mentioned in Copilot. All, except ChatGPT 4.0, recommended orthodontist consultation for personalized advice. BOS leaflet had the highest mean DISCERN score (4.70 ± 0.27), followed by Gemini (4.54 ± 1.03), AAO web-source (4.45 ± 0.75), Copilot (3.87 ± 1.64) and ChatGPT 4.0 (3.08 ± 1.65), revealing no significant difference. BOS and AAO were more readable than the LLMs. ChatGPT 4.0 was more readable among the LLMs but was still found to be difficult for the readers.

conclusionGuidelines have a superior narrative in terms of their detailed content and especially the justifications for the recommendations. Artificial Intelligence (AI)-supported LLMs provided understandable, simple and accurate information, despite lack of some details on certain topics. The readability of the responses from LLMs was difficult. Overall, patients should be advised that pre-trained algorithms should be used with caution as a source of information and that they should receive individual information from their orthodontists.

Indexed as

Artificial IntelligenceLanguageNutrition PolicyOrthodonticsPractice Guidelines as TopicGenerative Artificial IntelligenceHumansLarge Language ModelsOral HygieneSocieties, DentalArtificial intelligenceChatGPTCopilotGeminiLarge language models (LLMs)LeafletNutritionOrthodontics

Identifiers

PMID41299374
PMCPMC12659479

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LicenceCC BY-NC-ND
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Registered trials

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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.