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Artificial intelligence-assisted training for proximal caries diagnosis by undergraduate dental students: a
Rana Alkattan1,2,3, Lulwah Alreshaid4,5,6, Hayam Alfallaj4,5,6
1Department of Restorative and Prosthetic Dental Sciences, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, 11426, Saudi Arabia. dr_rkk@hotmail.com.
Background:
Artificial intelligence (AI) has shown promise in improving the detection of proximal caries on dental radiographs; however, its educational value in enhancing undergraduate students' independent radiographic interpretation remains unclear. Therefore, this study aimed to evaluate the effect of AI-assisted radiology training on undergraduate dental students' diagnostic competence in assessing proximal caries extension when compared with conventional radiology training.
Methods:
This randomized experimental study included 82 undergraduate dental students. After completing a pre-test using 20 conventional bitewing radiographs, from which students evaluated three proximal surfaces per radiograph, students were randomly allocated to either conventional radiology training or AI-assisted training using Second Opinion® software. Both groups received a one-hour training session. A post-test using 20 conventional bitewing radiographs without AI assistance was conducted one day later, with students again evaluating three proximal surfaces per radiograph. Diagnostic performance was assessed using total exact-match scores (0-60) and absolute average error (0-5 scale). Completion time in minutes was also recorded for pre- and post-tests. Mixed-design ANOVA was used to evaluate group, time, and interaction effects (α = 0.05).
Results:
Seventy-nine students completed both tests, with 41 trained using AI assistance and 38 trained using conventional radiography. No significant differences were found between training groups or across time points (pre- and post-intervention, p > 0.05). However, absolute average errors increased significantly in the conventional group from pre- to post-test (p < 0.05), indicating a greater deviation from the correct answers. Post-test completion time was significantly shorter in the AI group compared with the conventional group, and both groups demonstrated significant reductions in completion time from pre- to post-test (p < 0.05).
Conclusions:
AI-assisted training did not significantly improve students' diagnostic competency in proximal caries detection compared with conventional radiology training. However, AI training improved diagnostic efficiency, as indicated by the lower absolute average errors, and did not impair students' independent radiographic interpretation when AI support was removed. AI may serve as a supportive adjunct in undergraduate dental education, particularly for improving efficiency and consistency in lesion depth estimation.