Evidence map›Paper›PMID 42395591›Full record

SynthesisDepression and anxiety2026

Systematic Review and Exploratory Meta-Analysis of AI-Enabled and Digital Technology-Assisted Interventions for Dental Anxiety During Dental Treatment.

Mohammad Ali Saghiri, Ravinder S Saini, Ahid Amer Alshahrani, Sunil Kumar Vaddamanu, Ali Alqahtani, Nadim Z Baba

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in Depression and anxiety, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Mohammad Ali SaghiriDepartment of Restorative Dentistry, Director of Biomaterial and Prosthodontic Laboratory, Rutgers School of Dental Medicine, Newark, New Jersey, USA, rutgers.edu.ORCID 0000-0002-5064-7828
Ravinder S SainiDepartment of Allied Dental Health Sciences COAMS, King Khalid University, Abha, Saudi Arabia, kku.edu.sa.
Ahid Amer AlshahraniDepartment of Allied Dental Health Sciences COAMS, King Khalid University, Abha, Saudi Arabia, kku.edu.sa.
Sunil Kumar VaddamanuDepartment of Allied Dental Health Sciences COAMS, King Khalid University, Abha, Saudi Arabia, kku.edu.sa.ORCID 0000-0002-1583-3246
Ali AlqahtaniDepartment of Periodontics and Community Dentistry, King Khalid University, Abha, Saudi Arabia, kku.edu.sa.
Nadim Z BabaAdvanced Specialty Education Program in Implant Dentistry, School of Dentistry, Loma Linda University, California, USA, llu.edu.ORCID 0000-0002-6575-3885

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Dental anxiety adversely affects receiving dental care when needed. Artificial intelligence (AI)-enabled and digital technology-assisted interventions have been recently explored for assessment, monitoring or alleviation of anxiety during dental treatment. The aim of this systematic review was to summarise current evidence and review effectiveness of AI-enabled or digital technology-assisted interventions on anxiety and treatment-related outcomes in dentistry. Method: The review was conducted in accordance with Preferred Reporting for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Searches were conducted across ScienceDirect, PubMed, Scopus, Google Scholar and Cochrane Library. There were no date limitations applied. Search was restricted to human studies involving AI-enabled or digital technology-assisted interventions for dental anxiety management. Both interventional and observational studies were included for review. Data were extracted regarding study characteristics, intervention type, anxiety outcome measures, behavioural response, treatment adherence and satisfaction. Risk of bias was assessed utilising Mixed Methods Appraisal Tool (MMAT). Owing to significant clinical and methodological heterogeneity among the included studies, quantitative synthesis was limited to a small subset of comparable studies and was therefore considered exploratory. Results: Seven studies met our eligibility criteria. Eligible studies varied widely in design, population, intervention and outcome assessment modalities used. These were computerised cognitive behavioural therapy (CBT), humanoid robots, smartphone applications, machine learning (ML) approaches to assessment, AI-linked virtual reality (VR) or biofeedback programs. Narrative synthesis suggested that several interventions were associated with reductions in dental anxiety or improvements in patient behaviour. Quantitative pooling was possible for only a few studies which were found to be comparable. Our conclusions from this exploratory meta-analysis are limited by the small number of studies with comparison groups available and considerable heterogeneity. Conclusion: AI-enabled and digital technology-assisted interventions have potential to improve dental anxiety-related outcomes and dental patient experience. The evidence is currently limited. The available studies are small and heterogeneous and methodologically limited. Future investigations should include larger, robust trials with standardised outcome measures and consistent operational definitions of AI.

Indexed as

Artificial IntelligenceDental AnxietyDental CareDigital TechnologyDigital HealthHumans

Identifiers

PMID42395591
PMCPMC13324240

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.