ReviewEvidence-based dentistry2025
Artificial Intelligence in dentistry: an overview of systematic reviews and meta-analysis.
Review in Evidence-based dentistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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.
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Who cites it
4 citing papers in PubMed.
- Mechanobiology and Molecular Regulation of Periodontal Tissue Remodeling During Orthodontic Tooth Movement: From Periodontal Phenotype to Precision Orthodontics-A Narrative Review.International journal of molecular sciences · 2026Review
- Artificial Intelligence in Pediatric Dentistry: Current Applications, Emerging Trends, and Future Directions.Dentistry journal · 2026Review
- Article
- From remote screening to precision prevention: responsible multimodal AI for risk prediction and equitable oral healthcare.Frontiers in oral health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
AIM/
objectiveThis review assessed the quality and findings of systematic reviews on AI in dentistry, categorising advancements across various specialties.
methodsThe review analyzed data from seven databases, assessed review quality with ROBIS, calculated pooled AI performance estimates, and identified research gaps through an Evidence Gap Map.
resultsThis study analysed 116 included systematic reviews. Meta-analysis of twelve low-bias reviews showed AI diagnostic accuracy ranging from 82% to 95% across dental specialties. The pooled sensitivity and specificity of AI algorithms for dental diagnostics were 0.85 (95% CI: 0.76-0.93) and 0.93 (95% CI: 0.90-0.95), respectively. Advanced models, particularly Convolutional Neural Networks (CNN), demonstrated a pooled accuracy of 93.1% (95% CI: 91.19-95.05%). Corrected Covered Area analysis indicated low overlap among reviews (10%), reflecting the diverse applications of AI in dentistry. Significant heterogeneity across pooled sensitivity (I DISCUSSION: AI shows strong diagnostic accuracy across dental specialties like caries detection, cephalometric landmark identification, and oral lesion diagnosis, with pooled sensitivity (0.85), specificity (0.93), and AUC (0.95) values. Advanced AI models like CNNs, Artificial Neural Networks, and larger, diverse datasets improve diagnostic accuracy, especially in image classification. Addressing research gaps and standardising methods are key to optimizing AI's clinical impact.
conclusionThis review reinforces AI's transformative potential in dentistry, enhancing tasks like diagnosis, detection, and prognosis, particularly in caries and lesion detection, to improve clinical decision-making and patient outcomes.
Indexed as
Identifiers
41366014What OpenQuestion holds
Registered trials
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.