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Artificial Intelligence in Endodontic Education: A Systematic Review with Frequentist and Bayesian Meta-Analysis of Student-Based Evidence
2
Zitationen
3
Autoren
2025
Jahr
Abstract
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: > 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.
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