ArticleHealthcare (Basel, Switzerland)2025
The Impact of Language Variability on Artificial Intelligence Performance in Regenerative Endodontics.
Article in Healthcare (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Accuracy and Short-Term Consistency of Generative AI Chatbots in Guideline-Based Endodontic Decision Support: A Multi-Model Benchmarking Study.Dentistry journal · 2026Article
- Language-dependent performance variation in large language models for dental trauma management: a comparative evaluation of ChatGPT-5.2, Gemini 3.0, and Claude 4.5 Sonnet.BMC oral health · 2026Article
- Effect of language difference and time on the accuracy of artificial intelligence chatbots responses to questions about vital pulp therapy.BMC oral health · 2026Article
- Comparative assessment of AI-generated responses to frequently asked questions by parents on space maintainers in pediatric dentistry.BMC oral health · 2026Article
- The impact of language differences on the readability, quality, and reliability of information provided by artificial intelligence chatbots regarding vital pulp therapy: a cross-sectional study.BMC oral health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRegenerative endodontic procedures (REPs) are promising treatments for immature teeth with necrotic pulp. Artificial intelligence (AI) is increasingly used in dentistry; thus, this study evaluates the reliability of AI-generated information on REPs, comparing four AI models against clinical guidelines.
methodsChatGPT-4o, Claude 3.5 Sonnet, Grok 2, and Gemini 2.0 Advanced were tested with 20 REP-related questions from the ESE/AAE guidelines and expert consensus. Questions were posed in Turkish and English, with or without prompts. Two specialists assessed 640 AI-generated answers via a four-point rubric. Inter-rater reliability and response accuracy were statistically analyzed.
resultsInter-rater reliability was high (0.85-0.97). ChatGPT-4o showed higher accuracy with English prompts (
conclusionsThe performance of AI models varies significantly across languages. English queries yield higher accuracy. While AI shows potential for REPs information, current models lack sufficient accuracy for clinical reliance. Cautious interpretation and validation against guidelines are essential. Further research is needed to enhance AI performance in specialized dental fields.
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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.