Artificial Intelligence Vs Clinician Accuracy in Detecting Interproximal Caries: A Systematic Review

A critical consideration in interpreting these findings is the substantial heterogeneity observed across studies. Variability in AI model architecture, dataset characteristics, lesion classification criteria, reference standards, and validation methodologies contributed to differences in reported diagnostic performance Furthermore, many included studies were conducted in retrospective or controlled environments, which may overestimate performance compared to real-world clinical settings.
Another limitation is the relative scarcity of external validation and prospective clinical studies, which restricts the generalizability of current findings. Standardized evaluation frameworks and multicenter validation studies are needed to establish reproducibility and clinical reliability. Future research should therefore prioritize external dataset validation, prospective clinical trials, and the development of standardized benchmarking protocols to facilitate meaningful comparisons across studies
Overall, the evidence supports a hybrid diagnostic model in which AI systems enhance early lesion detection while clinicians provide contextual interpretation and treatment decision-making. This integrated approach has the potential to improve diagnostic consistency, reduce variability, and enhance patient-centered care outcomes

Artificial intelligence; Dental caries; Interproximal caries; Diagnostic accuracy; Bitewing radiography; Deep learning

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