ArticleJournal of pharmacy & bioallied sciences2025
Diagnostic Accuracy and Efficiency of AI-Assisted Radiographic Interpretation Compared to Conventional Methods in Early Detection of Dental Caries.
Article in Journal of pharmacy & bioallied sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Diagnostic Accuracy of Novel AI-Based Software in the Detection of Dental Caries on Bitewing and Intraoral Periapical Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- AI-assisted versus traditional radiographic interpretation for early caries detection.Bioinformation · 2026Article
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Authors and funding
6 authors.
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Abstract
Background: Early detection of dental caries is critical for implementing preventive measures and avoiding invasive treatments. Traditional radiographic interpretation by clinicians, though effective, is prone to variability. Artificial intelligence (AI)-assisted interpretation has emerged as a promising adjunct. Objective: To assess and compare the diagnostic accuracy and efficiency of AI-assisted radiographic interpretation with conventional clinician-based evaluation in the early detection of dental caries. Methods: A cross-sectional analytical study was conducted on 400 bitewing radiographs from 200 patients aged 18-40 years. Two groups were compared: Group A (conventional method) with three experienced clinicians, and Group B (AI-assisted method) using a convolutional neural network (CNN)-based software. The reference standard was consensus diagnosis after clinical validation. Diagnostic performance was evaluated using sensitivity, specificity, accuracy, and time-efficiency metrics. Results: AI-assisted interpretation showed significantly higher sensitivity (91.2% ± 3.5) than conventional interpretation (84.6% ±4.2; Conclusion: AI-assisted radiographic interpretation demonstrated superior diagnostic accuracy and markedly improved efficiency compared to conventional methods in early caries detection. AI tools can serve as reliable diagnostic adjuncts, enhancing clinical workflow and reducing diagnostic variability.
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