Artificial Intelligence in Detecting Early Tooth Decay and Influencing Treatment Decisions Among Dental Students and Experienced Dentists

What this Trial Is About

Researchers are evaluating how artificial intelligence AI affects the detection of proximal caries early tooth decay between teeth and the treatment decisions made by dental students and experienced dental professionals. The study compares the accuracy and decisions of early learners, such as dental students, with those of late learners, including dental residents and junior faculty. This research aims to understand how AI may influence diagnostic accuracy and treatment thresholds in dental education and practice. Participants will attend two visits about one week apart, during which they will review a randomized set of dental radiographs. At each visit, they will examine 10 images, some enhanced by AI and others not, and answer questions related to lesion severity, diagnostic accuracy, and treatment decisions. The study uses a randomized crossover design to compare the outcomes with and without AI assistance. During the study, participants diagnostic accuracy will be measured using several sensitivity and specificity metrics for lesion detection at different stages. They will complete surveys and answer questions during the visits. The primary outcomes focus on lesion severity assessments and diagnostic accuracy over approximately one week. The study is sponsored by the University of Michigan and is expected to complete in May 2027.

Artificial Intelligence (AI) for Proximal Caries Detection and Its Impact on Treatment Threshold