ArticleBMC oral health2026
Comparative assessment of the accuracy of different artificial intelligence models in answering analytical and knowledge-based questions in oral and maxillofacial radiology and oral and maxillofacial surgery.
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.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence models are increasingly used in healthcare education; however, their ability to handle both factual knowledge and analytical clinical reasoning in dentistry remains unclear. This study aimed to compare the accuracy of different AIs in answering knowledge-based and analytical multiple-choice questions in Oral and Maxillofacial Radiology (OMFR) and Oral and Maxillofacial Surgery (OMFS), and to evaluate performance differences according to cognitive task type.
methodsThis cross-sectional comparative study analyzed 258 multiple-choice questions from the Turkish Dental Specialty Examination (DUS) conducted between 2012 and 2021 (202 knowledge-based, 56 analytical). Five AI models (ChatGPT-5.2 Go, ChatGPT-5.2 Plus, DeepSeek V3, Claude Sonnet 4.5, and Gemini 3 Flash) answered all questions under default settings in a single session. Accuracy rates were compared using Chi-square and Kruskal-Wallis tests with Bonferroni correction. Inter-model agreement and reliability were assessed using Cohen's kappa and the intraclass correlation coefficient (ICC) (α = 0.05).
resultsSignificant differences among models were observed in knowledge-based questions (p = 0.048), analytical questions (p = 0.032), and overall accuracy (p = 0.006). Gemini achieved the highest accuracy in knowledge-based questions, while Claude demonstrated the lowest performance. Although a general difference was detected in analytical questions, pairwise comparisons did not show clear model superiority. Overall performance largely reflected success in knowledge-based tasks. Agreement analysis showed low kappa values (κ = 0.226-0.339) but moderate ICC levels (0.597-0.728).
conclusionsAIs demonstrate strong factual recall but remain limited in analytical clinical reasoning tasks. While these models may serve as supportive tools in dental education, their use as independent clinical decision-making systems is not yet reliable.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.