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Artificial Intelligence in Endodontic Education: A Systematic Review with Frequentist and Bayesian Meta-Analysis of
Carlos M Ardila1,2, Eliana Pineda-Vélez2,3, Anny M Vivares-Builes2,3
1Department of Periodontics, Saveetha Dental College, and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Saveetha 600077, India.
Background/Objectives:
Artificial intelligence (AI) is entering dental curricula, yet its educational value in endodontics remains unclear. This review synthesized student-based evidence on AI in endodontics, primarily comparing AI vs. students on diagnostic tasks as an educational endpoint and secondarily considering assessment tasks relevant to training.
Methods:
PubMed/MEDLINE, Embase, Scopus, and Web of Science were searched in July 2025. Eligible studies involved dental students using AI in endodontic tasks or applied AI to student-generated outputs. For diagnostic comparisons we performed random-effects meta-analysis and a complementary Bayesian random-effects model with weakly informative priors. Risk of bias used QUADAS-2; certainty used GRADE.
Results:
Five studies met inclusion. Two provided complete mean-SD data for the primary meta-analysis and one contributed to a sensitivity model after SD imputation; two were summarized narratively (AUC/F1 only). Pooled effects favored AI: Hedges g = 1.48 (95% CI 0.60-2.36; I2 ≈ 84%); sensitivity (k = 3) g = 1.45 (95% CI 0.77-2.14; I2 ≈ 77%). Across the two LLM studies with analyzable means/SDs, the pooled mean difference in accuracy was approximately +20 percentage points (AI - students). Bayesian analyses yielded posterior means near 1.5 with 95% credible intervals excluding 0 and P (μ > 0) ≈ 1.00. Educational outcomes were sparsely and non-standardly reported.
Conclusions:
Student-based evidence indicates that AI likely outperforms dental students on endodontic diagnostic tasks, supporting its use as an adjunct for formative tutoring, objective feedback, and more consistent assessment.
