Evidence map›Paper›PMID 41462454›Full record

ArticleBMC oral health2025

Assessment of artificial intelligence chatbots in responding to dental occlusion questions: a comparative study.

Hamod Alqahtani

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 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. Article
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.

Hamod AlqahtaniDepartment of Prosthetic Dental Sciences, College of Dentistry, Prince Sattam Ben Abdulaziz University, Al-Kharj, Saudi Arabia. hh.alqahtani@psau.edu.sa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) chatbots, which use large language models (LLMs) to comprehend user questions and respond to human conversations, are increasingly used in dental education, but their accuracy in core subjects like occlusion remains unexplored. This study evaluated the performance of four AI chatbots, namely, Claude 3.7 Sonnet, GPT-4o, DeepSeek, and Meta LLaMA 3.2, in answering multiple-choice questions (MCQs) related to fundamentals of dental occlusion.

methodsEach chatbot was tested across two rounds on a standardized set of MCQs taken from a textbook on dental occlusion. Accuracy, intra-model consistency, and inter-model agreement were assessed using descriptive statistics, Cohen's Kappa, and McNemar's test.

resultsClaude 3.7 sonnet and GPT-4o achieved the highest accuracy in round one at 90%, while in round two, Claude 3.7 sonnet had an accuracy of 91.4%, and GPT-4o scored 84.4%. Meta Llama3.2 recorded the lowest accuracy across both rounds, with 71.4% in the first and 64.3% in the second. The Kappa test showed the intra-model consistency between the two rounds to be highest for Claude 3.7 sonnet (0.746), with a McNemar P-value of 1. For inter-model consistency when comparing the chatbots in round 1, Claude 3.7 sonnet and GPT-4o displayed the highest consistency with a kappa value (p < 0.005) and McNemar P-value of 1. In round 2, Claude 3.7 sonnet with GPT-4o and with DeepSeek had the highest consistency, with kappa values of 0.272 and a McNemar P-value of 0.227.

conclusionsClaude 3.7 Sonnet and GPT-4o show strong potential as reliable tools for dental education. DeepSeek offers moderate reliability, while Meta LLaMA 3.2 displayed inconsistent performance. Human oversight remains critical, and further refinement is essential before broader adoption in academic or clinical settings.

trial registrationNot applicable as it is not a clinical trial.

Indexed as

Artificial IntelligenceDental OcclusionEducation, DentalGenerative Artificial IntelligenceHumansAccuracyArtificial intelligenceDental educationDental occlusionLarge language modelsMultiple-choice testsTechnology

Identifiers

PMID41462454
PMCPMC12859897

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.