An Inquiry into Dental Students' Perceptions of Artificial Intelligence in Dentistry: Examining their Beliefs, Attitudes, and Understanding

Year 2024, Volume: 25 Issue: 2, 169 - 181

Sena Aykut , Ayse Ege Selman , Burcu Karaduman

https://doi.org/10.69601/meandrosmdj.1522133

Abstract

Objective: Artificial intelligence (AI) is widely anticipated to become an integral component of dentistry soon given its potential to revolutionize both dental education and practice. Therefore, it is essential to understand the perspectives of dental students who will be the future practitioners to adopt and use these technologies effectively and efficiently. The study aimed to evaluate the beliefs, perceptions and attitudes of a sample of Turkish dental students towards AI.
Materials and Methods: Data was collected online from students regarding age, sex and academic year. The students' beliefs regarding AI were assessed using a 21-question survey form of AI Attitude Scale. Also, a 15-question survey form was used to investigate the opinions and knowledge of dental students about AI. A total of 527 dental students, aged 18 to 37 years, were recruited, including 142 first-grade, 14 second-grade, 171 third-grade, 90 fourth-grade, and 110 fifth-grade students.
Results: There was a significant difference in the mean belief dimension scores based on the sex of the students (p<0.05). Overall, the students had some awareness and slight agreement on the positive effects of AI (cost reduction and productivity increase). However, despite these benefits, most students viewed AI as a potential danger to their careers. Notably, females were found to be more in agreement on negative subdimensions of the scale, including concerns about unemployment, estrangement, environmental pollution, and disruption of supply-demand balances (p<0.05).
Conclusion: The study highlights the need for AI education in dental curriculum to prepare future practitioners and addresses their concerns.

Keywords

Artificial intelligence, attitudes, beliefs, dental students, dentistry, perceptions

Supporting Institution

This study was funded by The Scientific Technological Research Council of Turkey (1919B012005952)

References

• 1. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism, 2017;69: 36-40.
• 2. Cohen PR, Feigenbaum EA. The handbook of artificial intelligence (Vol. 3). California: Butterworth-Heinemann; 1982.
• 3. Frey CB, Osborne MA. The future of employment: How susceptible are jobs to computerisation? Technol Forecast Soc Change. 2017;114: 254-280.
• 4. Chen H, Zhang K, Lyu P, Li H, Zhang L, Wu J, Lee CH. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Scientific reports. 2019;9: 3840.
• 5. Manyika J, Lund S, Chui M, Bughin J, Woetzel J, Batra P, et al. Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Institute. 2017; 150: 1-148.
• 6. Agrawal P, Nikhade P, Nikhade PP. Artificial intelligence in dentistry: past, present, and future. Cureus. 2022;14: e2405.
• 7. Schwendicke F, Rossi JG, Gostemeyer G, Elhennawy K, Cantu AG, Gaudin R et al. Cost-effectiveness of artificial intelligence for proximal caries detection. Journal of Dental Research. 2021;100: 369-376.
• 8. Nguyen TT, Larrivée N, Lee A, Bilaniuk O, Durand R. Use of artificial intelligence in dentistry: current clinical trends and research advances. J Can Dent Assoc. 2021;87: 1488-2159.
• 9. Chen YW, Stanley K, Att, W. Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int. 2020;51: 248-257.
• 10. Yuzbaşıoğlu E. Attitudes and perceptions of dental students towards artificial intelligence. J Dent Educ, 2021; 85: 60-68.
• 11. World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. Jama. 2013;310: 2191-4.
• 12. Ferik IF. A study on the effects of artificial intelligence concepts. Istanbul: Marmara Univ; 2003.
• 13. Fishbein M, Ajzen I. Belief, attitude, intention, and behavior: An introduction to theory and research. MA: Addison-Wesley; 1975: 578 p.
• 14. Kwak Y, Seo YH, Ahn, JW. Nursing students' intent to use AI-based healthcare technology: Path analysis using the unified theory of acceptance and use of technology. Nurse Educ Today. 2022;119: 105541.
• 15. Jethlia A, Honey Lunkad DSAM, Arishi TQ, Humedi AY, Alsaab AI. Knowledge, attitudes and perceptions of intern and dental practioners in saudi arabia towards artificial intelligence. J Pharm Negat Results. 2022;13: 1161-1167.
• 16. Pinto dos Santos D, Giese D, Brodehl S, Chon SH, Staab W, Kleinert R et al. Medical students' attitude towards artificial intelligence: a multicentre survey. Eur Radiol. 2019;29: 1640-6.
• 17. Asmatahasin M, Pratap KVNR, Padma TM, Kalyan VS, Kumar VS. Attitude and perception of dental students towards artificial intelligence. Indian J Basic and Appl Med Res. 2021;10: 305-314.
• 18. Sudzina F. Do gender and personality traits (BFI-10) influence self-perceived tech savviness? Proceedings of 18th Int. Conference Information Technology for Practice. 2015; 87–94.
• 19. Dashti M, Londono J, Ghasemi S, Khurshid Z, Khosraviani F, Moghaddasi N et al. Attitudes, knowledge, and perceptions of dentists and dental students toward artificial intelligence: a systematic review. Journal of Taibah University Medical Sciences, 2024;19: 327-337.
• 20. Compeau DR, Higgins CA. Computer self-efficacy: Development of a measure and initial test. MIS quarterly. 1995;19: 189-211.
• 21. Wang YM, Wei CL, Lin HH., Wang SC, Wang YS. What drives students’ AI learning behavior: A perspective of AI anxiety. Interact Learn Environ. 2022; 1-17.
• 22. Bulut H, Kınoğlu NG, Karaduman B. The fear of artificial intelligence: dentists and the anxiety of the unknown. J Adv Res Health Sci. 2024;7: 55-60.
• 23. Ronquillo CE, Peltonen LM, Pruinelli L, Chu CH, Bakken S, Beduschi A et al. Artificial intelligence in nursing: Priorities and opportunities from an international invitational think‐tank of the Nursing and Artificial Intelligence Leadership Collaborative. J Adv Nurs. 2021;77: 3707-17.
• 24. European Commission, Directorate-General for Communication. Special Eurobarometer 460: Attitudes towards the impact of digitisation and automation on daily life. Accessed May 5, 2017 Available from: http://data.europa.eu/88u/dataset/S2160_87_1_460_ENG.
• 25. Karan-Romero M, Salazar-Gamarra, RE, Leon-Rios XA. Evaluation of attitudes and perceptions in students about the use of artificial intelligence in dentistry. Dent J. 2023;11: 125.