Knowledge, Attitudes and Perception towards Artificial Intelligence and Robotics in Dentistry - A Cross-Sectional Survey

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Abstract

OBJECTIVE: To evaluate dentists' knowledge, attitude and perception of robotics(R) and artificial intelligence (AI). METHODOLOGY: Data were gathered for this cross-sectional survey from dental schools in Karachi that were both public and private. A total of 550 surveys were delivered through Google Forms. Dental students, graduates, postgraduates, and professionals of both sexes aged 20-70 years were included. Participants were chosen by convenience sampling using non-probability. Data were analyzed using descriptive analysis and the Chi-square test in SPSS version 22.0. RESULTS: Of the 550 participants, men comprised 33% (n=181) overall, while women comprised 67%(n= 369). Despite having a great understanding of AI and R, the majority of participants, 55.1%(n=304), also have a strong point of view. However, only 42.5%(n=234) of respondents were aware of the difference between AI and R and 55.2%(n=304) have prior knowledge about AI and R. Nevertheless, 58% (n=319) of dentists thought it would be helpful for future dental procedures. Only 16%(n=88) of respondents believe artificial intelligence will eventually replace dentists. Some applicants, 55.2%(n=304), proposed employing AI/R for therapy, and 51%(n=281) agreed to get treatment. CONCLUSION: By evaluating dentists' knowledge, attitudes, and perceptions of these technologies, it is possible to understand better the implementation of robotics and AI in clinical practice. More education and training programmes for dental professionals would be beneficial, and more studies would be able to determine the best ways to integrate robotics and AI to enhance patient results.

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Zia, W., Kashif, M., Aleem, A., Raja, I. M., Rehman, A., & Butt, A. I. (2024). Knowledge, Attitudes and Perception towards Artificial Intelligence and Robotics in Dentistry - A Cross-Sectional Survey. Journal of the Liaquat University of Medical and Health Sciences, 23(1), 75–80. https://doi.org/10.22442/jlumhs.2024.01079

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