This study aims to evaluate the influence of artificial intelligence (AI) on the decision-making process for intervention after caries lesion detection. Participants will be dentists working in the Netherlands randomly divided into two groups. Dentists will be divided into two groups and receive a set of bitewing radiographs, which first will be evaluated with or without AI support according to their group. Participants will examine caries lesions on the radiographs and formulate treatment plans accordingly. Then, after a wash-out period of one month, the same radiographs, but in the opposite condition of AI support and again formulate treatment suggestions according to the present caries lesions.

This crossover randomized controlled trial evaluates the effect of artificial intelligence (AI) decision support on dentists' treatment planning following caries detection bitewing radiographs. The study targets clinical decision-making processes by assessing how AI influences diagnostic interpretation and subsequent treatment suggestions. Dentists will be randomly assigned into two study arms. Each participant will evaluate a standardized set of digital bitewing radiographs under two conditions: once with AI assistance and once without, separated by a one-month wash-out period to minimize recall bias. The AI tool provides caries detection prompts based on radiographic analysis but does not suggest treatment. The crossover design enables within-subject comparison, controlling for individual diagnostic thresholds. The radiographs remain constant across both phases to isolate the influence of AI support. The study focuses on diagnostic performance and clinical decision outcomes, both with and without AI support. Treatment decisions are categorized into three predefined levels: no treatment, non-invasive treatment (e.g., fluoride application, polishing, sealing), and invasive intervention (i.e., restorative treatment). Diagnostic accuracy is measured against a reference standard and reported in terms of sensitivity and specificity. Caries detection will be classified using a modified International Caries Classification and Management System (ICCMS). This study design allows to quantify AI's impact on diagnostic performance, as well as on potential shifts in treatment approach. The study aims to contribute to evidence-based guidance on the integration of AI tools into clinical dental practice.

Inclusion Criteria:

1. Graduated, practising dentists.
2. At least three years of experience

Exclusion Criteria:

1. Retired dentists.
2. Specialized practitioners (e.g., orthodontists and oral surgeons) if their typical practice does not involve routine caries diagnostics and treatment planning.