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Artificial Intelligence in dentistry: an overview of systematic reviews and meta-analysis

Evidence-Based Dentistry volume 26, page 180 (2025)Cite this article

Subjects

Abstract

Aim/Objective

This review assessed the quality and findings of systematic reviews on AI in dentistry, categorising advancements across various specialties.

Methods

The 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.

Results

This 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 (I2 = 98.26%), specificity (I2 = 87.49%), area under the curve (I2 = 86.62%) and accuracy (I2 = 75.86%) were observed.

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.

Conclusion

This 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.

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Fig. 1: PRISMA flow diagram illustrating the screening process.
Fig. 2: Corrected Covered Area (CCA) matrix of all included systematic reviews.Corrected covered area (CCA) matrix of all included systematic reviews.
Fig. 3: Study sensitivity, specificity, Accuracy, and AUC of AI models evaluated in high-quality systematic reviews.
Fig. 4: Outlier detection.

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Data availability

All data supporting the findings of this study are available from the corresponding author upon reasonable request and are largely included within the article and its supplementary materials.

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Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. UWA Dental School, The University of Western Australia, 17 Monash Ave, Nedlands, Perth, WA, Australia

    Ankita Saikia, Therese Kvist, Amr Fawzy & Robert Anthonappa

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  1. Ankita Saikia
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  2. Therese Kvist
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Contributions

AS: Contributed to conception, design, data acquisition, analysis, and interpretation, drafted and critically revised the manuscript. TK: contributed to the analysis or interpretation of data and critically revised the manuscript, and gave final approval. AF: contributed to the interpretation of data and critically revised the manuscript, and gave final approval. RA: Contributed to conception, design, interpretation, and critically revised the manuscript and gave final approval. All authors gave their final approval and agree to be accountable for all aspects of the work.

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Correspondence to Ankita Saikia.

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Saikia, A., Kvist, T., Fawzy, A. et al. Artificial Intelligence in dentistry: an overview of systematic reviews and meta-analysis. Evid Based Dent 26, 180 (2025). https://doi.org/10.1038/s41432-025-01190-z

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