MétaCan
Menu
Back to cohort
Record W4408998915 · doi:10.2196/65211

Teledentistry for Improving Access To, and Quality of Oral Health Care: Overview of Systematic Reviews and Meta-Analyses

2025· review· en· W4408998915 on OpenAlexafffund
Pascaline Kengne Talla, Paul Allison, André Bussières, Anisha Rodrigues, Frédéric Bergeron, Nicolas Giraudeau, Elham Emami

Bibliographic record

VenueJournal of Medical Internet Research · 2025
Typereview
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsUniversité LavalUniversité du Québec à Trois-RivièresMcGill UniversityMcGill University Health Centre
FundersMcGill University
KeywordsPreprintMeta-analysisMEDLINEMedicineQuality (philosophy)Systematic reviewHealth careInternet privacyWorld Wide WebComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Background: Digital interventions including teledentistry are promising approaches to address some of the inadequacies of health care systems. Despite existing systematic reviews (SRs) on the benefits, implementation challenges, accuracy, and effectiveness of teledentistry, a comprehensive synthesis of evidence on its impacts requires further analysis. Objective: The purpose of this overview of SRs is to summarize evidence on the impacts of teledentistry in promoting access to and enhancing the quality of oral health care. Methods: We searched electronic databases in MEDLINE (Ovid), Embase (Embase.com), CINAHL (EBSCO), Web of Science, Cochrane Library, and Epistemonikos from inception to March 2024, without date and language restrictions, to identify SRs and meta-analyses. Two independent reviewers performed data selection following the PICOSS (population, intervention, comparison, outcome, and study design) format, as well as the data extraction. We conducted quality assessments using both (A MeaSurement Tool to Assess Systematic Reviews-2) AMSTAR 2 and ROBIS (Risk Of Bias In Systematic reviews) tools. The certainty of evidence and the overlap of the primary studies included in the SRs were assessed. Results were presented in tables and graphs. A narrative synthesis was performed. Results: The search yielded 1020 articles, of which 30 SRs were included in the overview. The number of participants across these reviews ranged from 130 to 7913 people. All dimensions of the quality of care were addressed to varying extents, with the domains of effectiveness (22/30, 73%), patient-centered care (14/30, 47%), and efficiency (11/30, 37%) being the most extensively studied. Teledentistry addressed public health challenges by improving access to oral health care and reducing inequities (9/30, 30%) for vulnerable people. The major teledentistry applications were teleconsultation (13/30, 43%), and telediagnosis (9/30, 33%). Teledentistry enhanced patient-clinician communication, quality of life, and care experiences for both patients and providers. However, multilevel barriers must be addressed to ensure its successful implementation (7/30, 23%). Meanwhile, patient safety (8/30, 27%) and equity (1/30, 10%) were the least explored domains, with few reviews addressing adverse outcomes, as well as concerns related to data privacy (3/30, 10%) and confidentiality (2/30, 6%). Several SRs exhibited a critically low to low methodological quality (25/30, 83%) and a high risk of bias (8/30, 27%). The overlap (corrected covered area) of the primary studies in all the SRs was slight (30/30, 2.3%), while it was moderate (11/30, 5.7%) for SRs with meta-analyses. Conclusions: The findings of this overview suggest that teledentistry is an effective and efficient alternative to in-person oral health care. However, significant concerns regarding the quality of the reviews highlight an urgent need for more methodologically rigorous studies to generate robust and reliable evidence. This is particularly essential to better understand teledentistry's potential to enhance overall health outcomes and ensure equitable access to care, thereby providing a stronger foundation to guide clinical practices and inform policy decisions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.042
metaresearch head score (Gemma)0.076
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.474
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0420.076
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0090.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.825
GPT teacher head0.713
Teacher spread0.112 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations12
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Medical Internet ResearchSame topicDental Research and COVID-19French-language works237,207