Trial reportInternational endodontic journal2026
The Impact of Training Dental Students to Use an Artificial Intelligence-Based Platform for Pulp Exposure Prediction Prior to Deep Caries Excavation: A Proof-of-Concept Randomised Controlled Trial.
Trial report in International endodontic journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05912361 (The Impact of Training Dental Students for Using a Novel Artificial Intelligence-based Platform for Pulp Exposure Prediction Before Deep Caries Excavation), which is not on this map. Cited by 2 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.
The Impact of Training Dental Students for Using a Novel Artificial Intelligence-based Platform for Pulp Exposure Prediction Before Deep Caries Excavation: A Randomized Controlled Trial
Who cites it
2 citing papers in PubMed.
- Can artificial intelligence training improve clinical decision-making during deep caries excavation?Evidence-based dentistry · 2026Trial
- The Impact of Training Dental Students to Use an Artificial Intelligence-Based Platform for Pulp Exposure Prediction Prior to Deep Caries Excavation: A Proof-of-Concept Randomised Controlled Trial.International endodontic journal · 2026Trial
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
aimThis study evaluated the effect of a short, personalised training session on student performance in using an artificial intelligence (AI)-based platform for pulp exposure prediction before caries excavation and determined the required sample size for a further randomised controlled trial (RCT). METHODOLOGY: Undergraduate dental students were randomly assigned to the experimental (training) group and the control (no training) group. The training group received a 1-h training session before undertaking the experiment, focusing on the uses, applications, and drawbacks of AI and carious lesion penetration depth. The theoretical presentation was followed by practical exercises and a quiz to check learning progress. Later, participants in both groups completed an experimental task involving 292 cases. They were asked to predict pulp exposure using an AI-based website. Sample size calculations determined the required sample size, with 80% power and an alpha of 5%.
results18 participants were enrolled (9 in each group). The agreement between participants' decisions and AI predictions regarding the presence or absence of pulp exposure (agreeableness with AI) was higher in the training group compared to the control group (0.83 vs. 0.76). The training group had a slightly higher mean F1-score (0.63 vs. 0.62), accuracy (0.69 vs. 0.68), and sensitivity (0.63 vs. 0.59) than the control group. Based on the sample size calculation, at least 31 participants per group are needed for the future RCT.
conclusionsThe results support further investigation of customised training sessions prior to using an AI-based platform to assess their impact on dental students' agreement with AI predictions.
trial registrationClinicalTrial.gov identifier: NCT05912361.
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Registered trials
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