Data from: Impact of explainable artificial intelligence assistance on clinical decision making of novice dental clinicians

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.