Evidence map›Paper›PMID 42224218›Full record

ArticlePloS one2026

A comparative analysis of the performance of leading large language models on the endodontics section of the dentistry specialization exam in Türkiye.

Beyhan Başkan, Hatice Kübra Başkan, Nevzat Koç

Abstract readComparative Study
In one paragraph

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

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

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.

Beyhan BaşkanDepartment of Endodontics, Faculty of Dentistry, Kahramanmaraş Sütçü İmam University, Kahramanmaras, Türkiye.ORCID https://orcid.org/0000-0002-2463-4011
Hatice Kübra BaşkanDepartment of Pediatric Dentistry, Faculty of Dentistry, Kahramanmaraş Sütçü İmam University, Kahramanmaras, Türkiye.
Nevzat KoçDepartment of Endodontics, Faculty of Dentistry, Niğde Ömer Halisdemir University, Nigde, Türkiye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aimed to evaluate and compare the performance of eight contemporary LLMs on the endodontics section of the DUS, assessing their accuracy in both theoretical knowledge and simulated clinical scenarios from historical exam data.

methodsThe performance of eight different large language models (Claude 4, DeepSeek V3, Gemini 2.5 Pro, ChatGPT-4o, ChatGPT-5, Grok 4, LLaMA 4, and Perplexity) was evaluated using 127 multiple-choice endodontics questions from the Specialization Exam in Dentistry (DUS) administered by the Student Selection and Placement Center (ÖSYM) between 2012 and 2021. The models' responses were compared against the official answer keys. Statistical analyses were performed using Pearson's chi-square and McNemar tests, with a significance level of α = 0.05.

resultsSignificant differences existed among LLMs in overall accuracy (p < 0.001). Gemini 2.5 Pro achieved the highest accuracy (90.6%), outperforming ChatGPT-4o (61.4%) and LLaMA 4 (71.7%). In Clinical Practice Questions (CPQ), Gemini 2.5 Pro (93.9%) surpassed ChatGPT-4o (57.6%; p = 0.019). For General Knowledge and Concept Questions (GKCQ), Gemini 2.5 Pro (89.4%), Grok 4 (85.1%), and DeepSeek V3 (84.0%) exceeded ChatGPT-4o (62.8%; p < 0.001). No significant intra-model differences emerged between CPQ and GKCQ performance (p > 0.05).

conclusionContemporary LLMs demonstrate substantial competence in endodontic knowledge, with Gemini 2.5 Pro excelling in both theoretical and clinical queries. However, significant performance variability across models (61.4%-90.6%) and the complexity of retrieving and resolving clinical exam queries necessitate domain-specific optimization and expert oversight for reliable integration into dental education and practice.

Indexed as

Educational MeasurementEducation, DentalEndodonticsLarge Language ModelsHumans

Identifiers

PMID42224218
PMCPMC13225384

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.