EXAMINING THE IMPACT OF ARTIFICIAL INTELLIGENCE IN DENTISTRY: A COMPREHENSIVE SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: Artificial Intelligence (AI) in dentistry has the potential to revolutionize oral healthcare by solving its inherent shortcomings. Aim: To review and evaluate the body of research on artificial intelligence's use in dentistry, with a focus on how it affects treatment planning, diagnosis, and patient care in a range of dental specialties. Methodology: 30 papers encompassing oral diagnosis, surgery, endodontics, prosthodontics, orthodontics, forensic dentistry, radiography, and periodontics are thoroughly examined in this review using PRISMA guidelines. The Cochrane Handbook principles were followed in the evaluation of important variables such as randomization, blinding, withdrawal/dropout rates, sample size estimation, clarity of inclusion/exclusion criteria, examiner reliability testing, pre-specification of outcomes, and bias risk. The Newcastle-Ottawa Scale (NOS) was used in quality assessment to measure bias risk and star ratings. Results: The research highlight improvements in diagnosis, treatment planning, and procedural accuracy, illustrating the revolutionary effects of AI in dentistry. Applications of AI demonstrate its versatility and include automated designs, risk prediction, lesion recognition, and precision in dental operations. There is little chance of bias in randomization, intervention variations, and outcome assessments, according to the methodological evaluation, which shows excellent scientific rigor. Even though a few studies had minor issues including uneven blinding and missing data, these had no appreciable impact on the dependability of the results. Overall, the studies' consistent methodological quality highlights how AI may be relied upon to advance dental research and practice.
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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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".