SynthesisClinical and experimental dental research2026
Artificial Intelligence in Dental Treatment Planning and Diagnostic Decision-Making: A Systematic Review and Meta-Analysis.
Synthesis in Clinical and experimental dental research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Can artificial intelligence training improve clinical decision-making during deep caries excavation?Evidence-based dentistry · 2026Trial
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis systematic review and meta-analysis aimed to synthesize the available evidence on the use of AI in dental diagnostic decision-making and treatment planning, evaluating both diagnostic accuracy and its influence on clinical decision-making across different dental specialties and imaging modalities.
methodsA comprehensive search of MEDLINE, Embase, Cochrane CENTRAL, Web of Science, and Scopus was conducted from database inception to December 2025. Eligible studies evaluated AI algorithms used for dental diagnostic tasks or treatment planning and reported quantitative performance metrics or measurable decision-making outcomes. Random-effects meta-analyses were conducted to pool diagnostic performance measures.
resultsTwenty-seven studies involving 60,857 radiographic images were included. AI systems demonstrated a pooled sensitivity of 0.85 (95% CI: 0.76-0.91) and specificity of 0.94 (95% CI: 0.86-0.97). The pooled F1-score was 0.90 (95% CI: 0.77-0.96), and pooled precision was 0.88 (95% CI: 0.71-0.96). For segmentation tasks, the pooled Dice Similarity Coefficient was 0.89 (95% CI: 0.13-1.00). Substantial heterogeneity was observed across studies (I² > 95%). YOLO-based architectures achieved the highest performance for tooth detection and segmentation, with sensitivities approaching 99% and mean average precision exceeding 0.96. AI assistance also improved diagnostic efficiency and interobserver agreement while reducing diagnostic interpretation time.
conclusionsAI systems demonstrate strong diagnostic performance in dental imaging and decision support, particularly for tooth detection, segmentation, and pathology identification. However, substantial heterogeneity, retrospective study designs, and limited external validation highlight the need for rigorous prospective evaluation before widespread clinical implementation.
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