ArticleInternational journal of dentistry2026
Evaluating AI Chatbots in Prosthodontics Education: A Quantitative MCQ-Based Assessment.
Article in International journal of dentistry, 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Prosthodontics is a specialized branch of dentistry that encompasses a wide range of dental procedures, requiring advanced knowledge and expertise for both healthcare professionals. In this study, we assessed the performance of four widely accessible chatbots, ChatGPT-5, Claude 4, Microsoft Copilot, and DeepSeek-V3, using 150 clinical scenario-based prosthodontics multiple-choice questions (MCQs). Evaluating their performance is crucial to ensure reliability. Materials and Methods: A total of 150 questions compiled from Bootcamp in English were presented to the four chatbots. We then employed multiple evaluation criteria, such as bilingual evaluation understudy (BLEU), word error rate (WER), cosine semantic similarity, and Flesch reading ease (FRE) score, to provide a more comprehensive assessment of lexical similarity, error rate, semantic consistency, and readability. Results: Among the four chatbots, Microsoft Copilot (accuracy: 74%; confidence interval [CI]: 72.2%-76.2%) and ChatGPT-5 (accuracy: 73%; CI: 71.5%-75.8%) demonstrated the best overall performance, achieving high accuracy at 95% CI. Particularly, ChatGPT-5 showed the fewest word errors (WER: 7.86 [95% CI: 6.61-9.10]) and FRE readability of FRE: 20.22 (95% CI: 16.26-24.19) compared to Microsoft Copilot. In terms of alignment with reference outputs, based on the BLEU score, Claude 4 (0.0106 [95% CI: 0.0058-0.0154]) maintained higher scores compared to other analyzed chatbots. Collectively, ChatGPT-5 produced a high level of observed accuracy with relatively fewer errors, moderate alignment with the reference answers, and favorable readability. Conclusion: This study suggests that ChatGPT-5 demonstrated a favorable overall performance in prosthodontic clinical scenarios, with high observed accuracy, good textual and semantic alignment, relatively fewer errors, and favorable readability.
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.