ArticleBMC oral health2026
Performance of large language models in endodontics: accuracy, consistency, and benchmarking with consensus guidelines.
Article in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLarge language model (LLM)-based chatbots are increasingly used in healthcare, yet their diagnostic accuracy, consistency, and temporal stability in endodontics remain insufficiently evaluated. This study aimed to assess and compare the performance of LLM-based chatbots using established international clinical guidelines.
methodsA diagnostic accuracy study with a repeated-measures design was conducted. Two LLMs (ChatGPT-5 and Gemini-2.5 Flash) were evaluated using 200 structured yes/no items derived from consensus-based international position statements covering the full scope of endodontic practice. Each model was tested weekly over three consecutive sessions, yielding 600 responses per model. Accuracy was assessed against reference answers, consistency was analyzed using Fleiss’ κ and Cohen’s κ, and logistic regression was performed to evaluate the effects of model type, week, and clinical domain.
resultsChatGPT demonstrated significantly higher overall accuracy than Gemini (92.8% vs. 84.8%; OR = 2.37; p = 0.004) and near-perfect reproducibility (κ = 0.95), whereas Gemini showed fair agreement (κ = 0.38). Both models performed well in structured clinical domains but showed reduced accuracy in areas related to restoration and pulpal disease management.
conclusionsLLM-based chatbots show potential as decision support and educational tools in endodontics. However, performance limitations in complex clinical domains highlight the continued need for expert oversight in clinical decision-making.
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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.