ArticleBMC oral health2026
A bilingual evaluation of chatbot performance in bruxism-related information: accuracy and readability across two models.
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 (AI) chatbots have increasing applications in healthcare; however, their accuracy and readability across different languages remain unclear. Therefore, this study aimed to compare the performance of ChatGPT-5 and DeepSeek-V3 on bruxism-related questions in English and Turkish.
methodsResponses generated by ChatGPT-5 and DeepSeek-V3 to 20 questions were evaluated in Turkish and English, yielding four chatbot-language conditions. Accuracy was independently scored by two prosthodontists using a Modified Global Quality Score, and inter-rater reliability was assessed using the intraclass correlation coefficient (ICC). Readability was defined as the ease of reading and was assessed using the Flesch Reading Ease (FRE) scale for English texts and the Ateşman readability formula for Turkish texts. Descriptive statistics were calculated, differences in accuracy across conditions were analyzed using the Friedman test, and the relationship between accuracy and readability was examined using Spearman's rank correlation coefficient.
resultsAccuracy scores of responses generated by ChatGPT-5 and DeepSeek-V3 were generally high across both Turkish and English. No statistically significant differences in accuracy were observed among the four model-language combinations based on the Friedman test (χ² (3) = 3.204, p=0.361). Inter-rater reliability analysis demonstrated moderate agreement for single measures and good agreement for average measures between the two evaluators (ICC = 0.623 and 0.767, respectively). Comparison of Atesman readability scores and FRE-based English readability analyses revealed no statistically significant differences between the two chatbots, as confirmed by Mann-Whitney U tests (Turkish: p = 0.646; English: p = 0.745).
conclusionChatGPT-5 and DeepSeek-V3 demonstrated comparable accuracy in bruxism-related responses across Turkish and English. However, high readability levels limit patient accessibility, and no association was found between accuracy and readability, indicating that simplification is necessary for effective patient education.
Indexed as
Identifiers
What Socratic 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 Socratic graph.