READINESS OF DENTISTS TO APPLY ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN IMPLANT-SUPPORTED PROSTHODONTICS: A CROSS-SECTIONAL STUDY

BACKGROUND: Despite the rapid integration of artificial intelligence (AI) technologies into dentistry, including implant-supported prosthodontics, data on dentists’ readiness to use these tools in routine clinical practice remain limited. In the available literature, descriptive and review studies prevail, while structured assessments of professional knowledge and readiness for AI implementation are scarce.

AIM: To analyze the level of knowledge and readiness of dentists and senior dental students to apply artificial intelligence technologies in implant-supported prosthodontics.

METHODS: A cross-sectional observational comparative study was conducted. Practicing dentists (n = 30) and senior dental students (n = 23) were included. Inclusion criteria comprised a minimum of five years of clinical practice for dentists and enrollment in the senior years of dental education for students. Knowledge was assessed using an author-developed test consisting of 174 closed-ended questions, including a block on artificial intelligence technologies and a block on implant-supported prosthodontics. The primary outcome was the proportion of correct answers for the total test score and thematic blocks. Descriptive statistics and non-parametric intergroup comparisons were applied.

RESULTS: The mean proportion of correct answers for the entire test was 46.9% in dentists and 53.7% in students (p = 0.140). In the artificial intelligence block, students demonstrated a significantly higher proportion of correct answers compared to dentists (65.6% vs. 52.1%; p < 0.001). No statistically significant differences were found between groups in the implant-supported prosthodontics block (p = 0.10–0.11). The internal consistency of the test was high in both groups (Cronbach’s α > 0.8).

CONCLUSION: Senior dental students demonstrated a higher level of theoretical knowledge in artificial intelligence compared with practicing dentists, while overall knowledge in implant-supported prosthodontics was comparable between groups. These findings indicate the need to develop targeted educational programs aimed at supporting the safe and informed integration of AI technologies into clinical dental practice. The main limitations of the study are its cross-sectional design and limited sample size.