ArticleBMC oral health2025
Evaluation of the performance of large language models in clinical decision-making in endodontics.
Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of artificial intelligence in endodontic education: a systematic review.Frontiers in dental medicine · 2026Pooled it
- Evaluating ChatGPT-4o as an Educational Support Tool for the Emergency Management of Dental Trauma: Randomized Controlled Study Among Students.JMIR medical education · 2025Trial
- Accuracy and Short-Term Consistency of Generative AI Chatbots in Guideline-Based Endodontic Decision Support: A Multi-Model Benchmarking Study.Dentistry journal · 2026Article
- Accuracy and Error Patterns of References Generated by Large Language Models in Endodontics: The Role of Prompt Design and Model Selection.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Article
- A comparative evaluation of large language models in diagnosis and treatment planning in restorative dentistry.BMC oral health · 2026Article
- Comparative benchmark assessment of performance of six different large language models in clinical decision-making for vital pulp therapy.Journal of conservative dentistry and endodontics · 2026Article
- Can artificial intelligence applications be utilised in the management of dental trauma? A comparison of ChatGPT, Gemini, and DeepSeek.BMC oral health · 2026Article
- Can artificial intelligence diagnose the invisible? A scenario-based evaluation of AI performance in cracked tooth diagnosis.Odontology · 2026Article
- Large language models and generative artificial intelligence in endodontics: a scoping review.Odontology · 2026Review
- How Reliably Do Large Language Models Reproduce Vital Pulp Therapy Guidelines? A Mixed-Effects Evaluation of Guideline-Concordance and Error Directionality.Healthcare (Basel, Switzerland) · 2026Article
- Comparative Evaluation of Polymeric and Metal Ultrasonic Tips for the Removal of Calcium Hydroxide From Root Canals.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Evaluation of Arabic-Language AI Chatbot Responses to Migraine-Related Questions: A Comparative Cross-Sectional Study.Journal of clinical medicine · 2026Article
- Evaluating large language models for endodontic case difficulty assessment: accuracy, consistency, temporal stability, and agreement.BMC oral health · 2026Article
- Are AI chatbots ready for endodontics? Evaluating their validity, consistency, and readability in patient-oriented responses.Odontology · 2026Article
- Generative Artificial Intelligence: Applications and Future Prospects in Dentistry.International dental journal · 2026Review
- Article
- Performance comparison of large language models on pediatric dentistry questions in the Turkish dentistry specialization examination.BMC medical education · 2025Article
- Assessment of artificial intelligence chatbots in responding to dental occlusion questions: a comparative study.BMC oral health · 2025Article
- Comparative evaluation of ChatGPT and Gemini in detecting external apical root resorption on panoramic radiographs of orthodontic patients : Artificial intelligence performance in detecting root resorption.Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) chatbots are excellent at generating language. The growing use of generative AI large language models (LLMs) in healthcare and dentistry, including endodontics, raises questions about their accuracy. The potential of LLMs to assist clinicians' decision-making processes in endodontics is worth evaluating. This study aims to comparatively evaluate the answers provided by Google Bard, ChatGPT-3.5, and ChatGPT-4 to clinically relevant questions from the field of Endodontics.
methods40 open-ended questions covering different areas of endodontics were prepared and were introduced to Google Bard, ChatGPT-3.5, and ChatGPT-4. Validity of the questions was evaluated using the Lawshe Content Validity Index. Two experienced endodontists, blinded to the chatbots, evaluated the answers using a 3-point Likert scale. All responses deemed to contain factually wrong information were noted and a misinformation rate for each LLM was calculated (number of answers containing wrong information/total number of questions). The One-way analysis of variance and Post Hoc Tukey test were used to analyze the data and significance was considered to be p < 0.05.
resultsChatGPT-4 demonstrated the highest score and the lowest misinformation rate (P = 0.008) followed by ChatGPT-3.5 and Google Bard respectively. The difference between ChatGPT-4 and Google Bard was statistically significant (P = 0.004).
conclusionChatGPT-4 provided more accurate and informative information in endodontics. However, all LLMs produced varying levels of incomplete or incorrect answers.
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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.