Performance of Free Web-Based Multimodal Large Language Models on Turkish and English Image-Based Dental Examination Questions
N Ersan, U A Ova, M M AtapekAbstract
Objectives
To compare final-answer accuracy of web-based large language models (LLMs) on Turkish and English image-based dental examination multiple-choice questions (deMCQs), and to describe explanation quality, reference quality, output volume, and an exploratory word-normalized water-use scenario.
Methods
Fifty-two image-based deMCQs (2012–2025) were evaluated in Turkish and English using ChatGPT, Gemini, and Claude, yielding 312 responses. Accuracy was coded as correct or incorrect. Explanation and reference quality were assessed using a 5-point Likert scale and treated as exploratory descriptive outcomes. Accuracy was analyzed using question-clustered binomial generalized estimating equations (GEE) with model, language, and model×language interaction terms. Output word counts were summarized, and a study-specific exploratory word-normalized water-use scenario was derived from a published exchange-level estimate.
Results
Overall accuracy was 56.7% (177/312). Accuracy was 59.0% in Turkish and 54.5% in English. GEE analysis showed no significant effects of model, language, or model×language interaction (p > 0.05). Descriptively, Gemini had the highest accuracy (61.5%), whereas Claude had the highest mean explanation (3.95±1.31) and reference quality (4.85±0.38) scores. Across 312 prompt–response exchanges, the models generated 34,534 words, corresponding to an exploratory word-normalized water-use proxy of 3.12–7.80 L.
Conclusions
Freely accessible web-based LLMs demonstrated moderate accuracy on image-based deMCQs. The water-use proxy was derived from published exchange-level assumptions and should not be interpreted as a direct measurement or validated model-specific estimate of water consumption. Descriptive differences between models and languages should not be interpreted as evidence of superiority or equivalence. These systems may support dental education; however, outputs require expert verification before educational or clinical use.
Advances in knowledge
This study evaluates three widely accessible web-based multimodal LLMs using an entirely image-based dental examination question set, accounts for repeated observations through question-clustered GEE, compares Turkish and English performance without equating non-significance with equivalence, and presents a transparent word-normalized environmental scenario with explicit assumptions and limitations.