Can artificial intelligence training improve clinical decision-making during deep caries excavation?

A COMMENTARY ON: Ramezanzade S, Dascalu TL, Bakhshandeh A, Uribe SE, Ibragimov B, Bjørndal L. 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 randomized controlled trial. Int Endod J. 2026;59:1248-56. https://doi.org/10.1111/iej.70046 DESIGN: This randomized controlled trial (RCT) evaluated whether a structured educational intervention could improve dental students' interaction with an artificial intelligence (AI)-based decision-support system developed to predict pulp exposure before excavation of deep carious lesions. CASE SELECTION: Eighteen dental students were randomly allocated to either an experimental group receiving a one-hour personalized training session on the use of the AI platform or a control group receiving a brief introductory video. Participants subsequently completed a case-based assessment involving radiographic evaluation of deep carious lesions and prediction of pulp exposure risk using the AI system. DATA ANALYSIS: The primary outcome was agreement with AI recommendations ("agreeableness with AI"). Secondary outcomes included diagnostic accuracy, sensitivity, specificity, F1-score, and response time. Outcomes were compared between groups, and the findings were used to estimate the sample size required for a future definitive trial. RESULTS: Participants who received AI-focused training demonstrated greater agreement with AI recommendations than controls. However, improvements in agreement were not accompanied by meaningful differences in diagnostic accuracy, sensitivity, specificity, or F1-score. Response times were slightly shorter among trained participants. The findings suggest that targeted instruction may influence how users interact with AI systems, although objective diagnostic performance remained largely unchanged. CONCLUSIONS: A short, personalized training session may increase dental students' agreement with AI-generated predictions of pulp exposure during deep caries excavation. However, the intervention did not substantially improve diagnostic performance, and no patient-centered outcomes were assessed. Larger studies are needed to determine whether AI training can enhance clinical decision-making and improve outcomes relevant to the management of deep carious lesions.