ArticleBMC oral health2026
Performance of AI chatbots in answering questions on tooth bleaching: a multilingual comparative study.
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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) chatbots are increasingly used in healthcare to provide health-related information and answer patient questions. However, their reliability in specialized dental fields such as restorative dentistry remains insufficiently evaluated. This study aimed to evaluate and compare the accuracy and consistency of responses generated by five artificial intelligence chatbot systems-ChatGPT-3.5 and ChatGPT-4 (OpenAI), Bing Chat (Microsoft), Gemini (Google), and Claude-Instant (Anthropic)-regarding dental bleaching.
methodsFifteen frequently asked questions about dental bleaching, identified by restorative dentistry specialists, were categorized as undergraduate-or specialist-level questions. All questions were submitted to five artificial intelligence chatbots in both Turkish and English. Each question was asked three times per day over three consecutive days using standardized prompts. Responses were independently evaluated by two experts using a five-point Likert scale, and mean scores were calculated. A three-way ANOVA was conducted to assess the effects of chatbot type, knowledge level, and question language on response accuracy. Inter-rater agreement between evaluators was assessed using Cohen's kappa coefficient. Statistical significance was set at p < 0.05.
resultsChatbot type had a significant effect on response accuracy (p < 0.001, η² = 0.405). ChatGPT-4 showed the highest accuracy, followed by Claude-Instant and GPT-3.5, whereas Gemini and especially Bing Chat demonstrated significantly lower performance (p < 0.001). Question language and knowledge level showed no significant main effects (p > 0.05). Significant interactions were observed between chatbot type and knowledge level and between chatbot type and language (p < 0.001). No significant differences were observed across days or time periods (p > 0.05).
conclusionsThe accuracy of chatbot-generated information regarding dental bleaching depends strongly on the specific AI model used. Advanced large language models, particularly ChatGPT-4, generate more accurate and consistent responses than other evaluated systems. AI chatbots should therefore not be considered interchangeable sources of clinical information, and their outputs should be interpreted cautiously and verified with professional guidance. CLINICAL RELEVANCE: These findings highlight the importance of critically evaluating AI-generated health information and emphasize that chatbot responses should not replace professional clinical consultation.
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.