TELE-DENTISTRY IN RURAL AND UNDERSERVED POPULATIONS: A SYSTEMATIC REVIEW OF ACCESS AND TREATMENT OUTCOMES-A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background Tele-dentistry has emerged as a promising solution to address oral health disparities in rural and underserved populations, where access to dental care remains limited due to geographic, economic, and workforce barriers. Despite growing interest in digital health platforms, the effectiveness and clinical utility of tele-dentistry in these settings remain inadequately synthesized in the literature. Objective This systematic review aims to evaluate the impact of tele-dentistry on access to care and treatment outcomes in rural and underserved populations compared to traditional in-person dental services. Methods A systematic review was conducted following PRISMA guidelines. Databases searched included PubMed, Scopus, Web of Science, and Cochrane Library from 2018 to 2024. Keywords combined using Boolean operators included "tele-dentistry," "rural," "underserved," "oral health," and "dental outcomes." Eligible studies included randomized controlled trials, cohort studies, and cross-sectional designs that assessed tele-dentistry in rural or underserved populations. Two reviewers independently screened studies, extracted data using a standardized form, and assessed risk of bias using the Cochrane Risk of Bias Tool and Newcastle-Ottawa Scale. Results Eight studies involving diverse populations across Australia, the United States, and India were included. Findings demonstrated that tele-dentistry significantly improved access to care, with high patient satisfaction and diagnostic accuracy comparable to traditional consultations (p < 0.05). However, variability in technology use and study design limited quantitative synthesis. Conclusion Tele-dentistry appears to be a clinically effective and accessible alternative for delivering dental care in underserved settings. While evidence supports its integration into routine practice, further large-scale, standardized studies are needed to evaluate long-term outcomes and implementation challenges.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".