Performance of Free Web-Based Multimodal Large Language Models on Turkish and English Image-Based Dental Examination Questions

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.

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.

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.

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.

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.