Evaluation of a no-code AI model for detecting periapical radiolucencies: impact of anatomical region on diagnostic performance

This no-code AI model demonstrated high accuracy in detecting apical radiolucent lesions and revealed that diagnostic performance can vary based on anatomical location. The model's increased sensitivity in the maxilla and higher specificity in the mandible highlight the relevance of regional anatomy in AI-assisted diagnostics. These findings support the potential of no-code AI platforms as accessible tools for building clinically meaningful diagnostic models and underscore the importance of anatomical considerations in their implementation.