Knowledge, attitude, and perception of dentists regarding the role of Artificial Intelligence and its applications in Oral Medicine and Radiology: a cross sectional study

Abstract

Background and objective: There is a paradigm shift in the medical and dental fields due to the introduction of artificial intelligence (AI). Since AI has a potential impact on current and future practitioners, understanding the basic concept, working principle, and likely applications of AI as a diagnostic tool in Oral Medicine and Radiology is necessary for its widespread use. Therefore, this study aims to assess the knowledge, attitude, and perception of dental students and dentists regarding the possible applications of AI in the field of Oral Medicine and Radiology. Materials and methods: This was a cross-sectional questionnaire-based study comprising 15 questions circulated through Google Forms® to 460 dental students and professionals. The questionnaire collected demographic data of participants and assessed their knowledge, perception, and attitude about AI in Oral Medicine and Radiology answered using a 5-point Likert scale. Responses obtained were statistically analyzed using descriptive statistics and a chi-square test. Results: Out of 460 participants, majority had an idea about AI (94.13%) and its working principle (73.30%). Participants agreed that AI can be used in the diagnosis and formulating of treatment plans (88.47%), early detection of cancer (77.82%), forensic dentistry (74.13%), and as a prognostic (80.65%) and quality control tool (81.30%). A majority felt that AI should be incorporated into the dental curriculum (92.39%) and most of them were against suggesting AI in clinical incorporation (35.87%) with a fear that AI might replace the clinician in the future (76.52%). Conclusion: Based on the findings of the study, we strongly recommend that further research and insights into AI should be delivered through lectures, curricular courses, and scientific meetings to explore and increase awareness about this fascinating technology.

Keywords

• artificial intelligence
• oral medicine
• deep learning
• machine learning
• attitude-perception