ArticleBMC oral health2026
Effect of language difference and time on the accuracy of artificial intelligence chatbots responses to questions about vital pulp therapy.
Article in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
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–based chatbots are increasingly being used as supportive tools in healthcare for accessing and interpreting medical information. However, variations in language and temporal updates may influence the accuracy and consistency of the information they provide. This study aimed to evaluate the effects of language difference (Turkish and English) and time (10 days) on the accuracy of responses provided by six different artificial intelligence (AI) chatbots (ChatGPT, ChatGPT-4o, ChatGPT-5, Gemini, Microsoft Copilot, and Perplexity) to questions related to vital pulp therapy.
methodsTwenty questions were prepared in accordance with the clinical guidelines of the American Association of Endodontists (AAE) and were presented to each model in both Turkish and English three times a day (morning, noon, and evening) over a 10-day period. A total of 7,200 responses were collected. The responses were classified as correct or incorrect by two independent researchers. The accuracy rates of the groups were analyzed using descriptive statistics, and Chi-square tests to compare correct versus incorrect response ratios.
resultsMicrosoft Copilot achieved the highest accuracy rate (87.3%), while Perplexity showed the lowest (77.2%). When evaluated by language, ChatGPT-4o performed best in Turkish questions (83.8%), whereas Microsoft Copilot demonstrated the highest accuracy in English questions (87.3%). The time variable had no statistically significant effect on accuracy.
conclusionsThese results indicate that AI chatbots can serve as supportive tools in clinical decision-making processes; however, language differences and model-dependent performance variations should be carefully considered.
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.