<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>13(11)</volume><submitter>Ardila CM</submitter><pubmed_abstract>&lt;h4>Background/objectives&lt;/h4>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.&lt;h4>Methods&lt;/h4>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.&lt;h4>Results&lt;/h4>Five stud</pubmed_abstract><journal>Dentistry journal</journal><pagination>489</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12650973</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Artificial Intelligence in Endodontic Education: A Systematic Review with Frequentist and Bayesian Meta-Analysis of Student-Based Evidence.</pubmed_title><pmcid>PMC12650973</pmcid><pubmed_authors>Pineda-Velez E</pubmed_authors><pubmed_authors>Vivares-Builes AM</pubmed_authors><pubmed_authors>Ardila CM</pubmed_authors></additional><is_claimable>false</is_claimable><name>Artificial Intelligence in Endodontic Education: A Systematic Review with Frequentist and Bayesian Meta-Analysis of Student-Based Evidence.</name><description>&lt;h4>Background/objectives&lt;/h4>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.&lt;h4>Methods&lt;/h4>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.&lt;h4>Results&lt;/h4>Five stud</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Oct</publication><modification>2026-05-19T03:17:19.919Z</modification><creation>2026-05-19T03:11:30.039Z</creation></dates><accession>S-EPMC12650973</accession><cross_references><pubmed>41294470</pubmed><doi>10.3390/dj13110489</doi></cross_references></HashMap>