aceModerate evidence
Published recordEducationGood Space
More
Artificial Intelligence in Endodontic Education: A Systematic Review with Frequentist and Bayesian Meta-Analysis of Student-Based Evidence
- Truvace record
- TRV-2026-2829raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Systematic review or meta-analysis
- Sources
- 1 sourcePeer-reviewed
- sha256
- 3b05a406877c…check it
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…
The gain
A systematic review of five studies found AI models beat dental students on endodontic diagnostic tasks, with a pooled Hedges g of 1.48 and roughly 20 percentage points higher accuracy in the two chatbot studies with usable data.
What this doesn’t fix
Only two studies fed the primary meta-analysis, heterogeneity was high (I2 about 84%), and no study measured learning outcomes, engagement or trust with validated tools or pre/post designs.
Sources
1 on file- Dentistry Journaldoi:10.3390/dj13110489
What the review found when it combined the studies
These are the review’s own pooled results, copied from its abstract. Truvace did not recalculate them.
Standardised difference in endodontic diagnostic performance, AI versus students (Bayesian, three studies)
Posterior mean SMD 1.49 (95% CI 1.05 to 1.96)
3 studies · search to July 2025
The review’s own words
When the study with imputed SDs [ 21 ] was included, the posterior mean was μ = 1.49 (95% CrI 1.05–1.96)
Diagnostic performance of AI versus dental students on endodontic tasks (sensitivity analysis with imputed SDs)
Hedges' g 1.45 (95% CI 0.77 to 2.14)
3 studies · search to July 2025
The review’s own words
A sensitivity model adding the study with imputed SDs [ 21 ] gave a very similar estimate (g = 1.45; 95% CI 0.77–2.14; I 2 ≈ 77%).
Diagnostic performance of AI versus dental students on endodontic tasks (primary analysis)
Hedges' g 1.48 (95% CI 0.60 to 2.36)
search to July 2025
The review’s own words
For the primary meta-analysis [ 20 , 23 ], the pooled effect was Hedges g = 1.48 (95% CI 0.60–2.36) with substantial heterogeneity (I 2 ≈ 84%).
Standardised difference in endodontic diagnostic performance, AI versus students (Bayesian, two studies)
Posterior mean SMD 1.48 (95% CI 0.98 to 1.99)
2 studies · search to July 2025
The review’s own words
yielded a posterior mean for the overall standardized mean difference of μ = 1.48 (95% CrI 0.98–1.99)
Code checks every number above against the review’s text before it is shown. The short labels are an AI model’s reading of that text, so the review’s own sentence sits under each one. A pooled figure is only as strong as the studies behind it. How Truvace uses pooled results
Cite this record
v1Truvace Impact Record TRV-2026-2829, v1: “Artificial Intelligence in Endodontic Education: A Systematic Review with Frequentist and Bayesian Meta-Analysis of Student-Based Evidence.” Truvace, 2026-11-12. /record/TRV-2026-2829 (accessed on the day you copy it). sha256 3b05a406877c9216…
Change history
1 versionEvery change made to this record since it was first published, and why. Nothing has changed since it went up.
Certified into the record
Check this record hasn’t been altered
How to check it yourself, without trusting this page
Download any version from /api/record/TRV-2026-2829 and hash its canonical field yourself, for example with shasum -a 256. You should get its content_hash. Each version’s text also ends with prev:and the hash of the version before it (version 1 points to 64 zeros). If anyone had changed a single character of any version after it was saved, those hashes wouldn’t line up.