Trial reportBMC medical education2026
Artificial intelligence-assisted training for proximal caries diagnosis by undergraduate dental students: a randomized experimental study.
Trial report in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
8 authors.
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Abstract
backgroundArtificial 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.
methodsThis 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
resultsSeventy-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).
conclusionsAI-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.
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