Accuracy of deep learning-based AI models for early caries lesion detection: the influence of annotation quality and reference choice

Our findings indicate a strong influence of reference standards on AI model evaluation. While annotation strategies during training did not significantly affect AI accuracy in caries lesion segmentation, evaluation was subject to bias when models were tested against different reference standards. CLINICAL RELEVANCE : AI-based tools for caries detection are becoming common in dentistry. This study shows that how these models are evaluated can significantly impact perceived performance. Clinicians and developers should ensure that evaluation standards are independent and clinically relevant to avoid overestimating AI's diagnostic abilities and to build trust for real-world use and regulatory approval.