Application and performance of artificial intelligence in implant dentistry: An umbrella review
Bibliographic record
Abstract
This umbrella review evaluates the applications and performance of AI in implant dentistry, comparing its effectiveness to human intelligence. The PICO question addressed was: “In implant dentistry, how does the performance of AI-driven approaches compare to standard references or conventional methods performed by human practitioners?” A comprehensive search was conducted across databases such as PubMed, Embase, Scopus, Web of Science, PROSPERO, Cochrane Library, and Google Scholar until August 2024. Two independent reviewers conducted the screening, data extraction, quality assessment, and certainty evaluation. After screening 12 studies included for qualitative analysis. The majority of studies utilized deep learning (DL) models, and some studies employed traditional machine learning (TML) or simple rule-based algorithms. Most systematic reviews found AI applications and performance promising when compared to human intelligence. However, several challenges were identified, particularly in AI’s accuracy in measuring bone width and height, detecting the inferior alveolar canal, treatment planning, and predicting osseointegration. Although AI shows promise in detecting anatomical landmarks (such as the maxillary sinus), identifying implant systems, and supporting clinical decisions, current models still face significant limitations and should not yet be considered as standalone tools capable of replacing human practitioners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".