Objective: Despite artificial intelligence (AI) is being used increasingly in healthcare, implementation challenges exist leading to potential biases during the clinical decision process of the practitioner. The interaction of AI with a novice clinician was investigated through an identification task, an important component of diagnosis, in dental radiography. The study evaluated the performance, efficiency, and confidence level of dental students on radiographic identification of FI, with and without AI assistance.

Materials and Methods: 22 third- and 19 fourth-year dental students (DS3 and DS4, respectively) completed remotely administered surveys to identify FI lesions on a series of dental radiographs. The control group received radiographs without AI assistance while the test group received the same radiographs and AI-labeled radiographs. Data was appropriately analyzed using the Chi-square, Fischer’s exact, ANOVA or Kruskal-Wallis tests. 

Results: Performance between groups with and without AI assistance was not statistically significant except for one question where tendency was to err with AI generated answer (p<0.05). The efficiency of task completion and confidence levels were not statistically significant between groups. However, both groups with and without AI assistance believed the use of AI would improve the clinical decision-making. 

Discussion: Dental students detecting FI in radiographs with AI assistance had a tendency towards over-reliance on AI. Conclusion: AI input impacts clinical decision-making, which might be particularly exaggerated in novice clinicians. As it is integrated into routine clinical practice, caution must be taken to prevent over-reliance on AI-generated information.