ArticleBMC oral health2026
Quality, completeness, readability, and response time of AI chatbots in the management of deep caries and pulp exposure: a bilingual 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) chatbots are being increasingly used to obtain health-related information; however, the quality, completeness, readability, and consistency of AI-generated responses across different languages remain insufficiently investigated in endodontics, particularly regarding the management of deep caries and pulp exposure.
methodsThirty open-ended questions were developed on the basis of the 2019 European Society of Endodontology (ESE) position statement and the current literature. The questions were submitted in English and Turkish to ChatGPT-4o, ChatGPT-5, Consensus Pro, and Perplexity Pro. The responses were independently evaluated by two blinded endodontists via the modified DISCERN (mDISCERN), the Global Quality Score (GQS), and a three-point completeness scale. Readability was assessed via the Flesch Reading Ease Score (FRES) for English responses and the Ateşman Readability Index for Turkish responses. Data were analyzed using two-way analysis of variance (ANOVA), intraclass correlation coefficient (ICC) analysis, and post hoc multiple comparisons.
resultsAI chatbots and language significantly affected readability and response time (p < 0.05). Turkish responses demonstrated higher readability scores, particularly for the ChatGPT-4o and ChatGPT-5. Perplexity generated the shortest response times, whereas Consensus had the longest response times. The quality scores (mDISCERN and GQS) were significantly influenced by the AI model but not by language. Consensus achieved the highest quality ratings in both languages.
conclusionsAI model selection had a greater influence on response quality than language did. Consensus achieved the highest expert-rated quality and completeness scores, whereas Perplexity generated the fastest responses. These findings highlight the importance of carefully selecting AI tools when seeking evidence-based endodontic information across different languages.
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.