SHOULD ARTIFICIAL INTELLIGENCE INTEGRATE WITH DENTAL EDUCATION? AN ASSESSMENT THROUGH THE DENTOMAXILLOFACIAL RADIOLOGY PERSPECTIVE

Cansu Buyuk

Abstract


Introduction: The requirement to adapt dentistry education to the growing knowledge and big data is evident. Future dentists will participate in AI studies as both researchers and users. The main aim was to evaluate the attitudes of undergraduate dental students on artificial intelligence (AI) applications. Secondarily, it was aimed to discuss possible solutions for the integration of AI into education in particular to dentomaxillofacial radiology.

Material and Method: A written survey included 16 questions with a 5-point Likert-scale was designed. The content of the survey included basic knowledge about AI terminology, applications on dentomaxillofacial radiology, and future estimations. One hundred seventy-six students attending the 3rd, 4th, and 5th grades were included. The attitudes of the students were assessed with the total score. The responses were scored as: Strongly disagree: 1, Disagree: 2, Neutral: 3, Agree: 4, Strongly agree: 5. The minimum and maximum possible points were 16 and 80, and 48 was the middle score. The scores were classified as 16-31 (group 1), 32-47 (group 2), 48-63 (group 3), and 64-80 (group 4). Cronbach's alpha reliability coefficient was used to test the internal consistency of the questions. One-way analysis of variance and Chi-square test were used to compare normally distributed data. The Mann-Whitney U test was used to compare the that did not show normal distribution. Statistical significance was evaluated with a 5% Type-I error level.

Results: The Cronbach's alpha reliability coefficient was 0.849. The response rate of the participants was 83.41% (n=176). The mean total scale score was 57.68 ± 0.651. Group 3 had the largest cluster (67.61%; n=119), whereas the group 1 had the smallest (0.56%; n=1). The total scale score showed no statistically significant difference between the academic years.

Conclusion: The attitudes of undergraduate dental students on AI were positive and students are aware of the potential of applications in the field. The conventional dentomaxillofacial radiology curriculum requires an update.


Keywords


Artificial intelligence. Machine learning. Dentistry. Dentomaxillofacial radiology. Undergraduate student



DOI: http://dx.doi.org/10.19177/jrd.v9e120216-13

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Journal of Research in Dentistry, University of Southern of Santa Catarina, Santa Catarina, ISSN 2317-5907

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