Exploring dental faculty awareness, knowledge, and attitudes toward AI integration in education and practice: a mixed-method study.

Abstract

Background: Dentistry is shifting from traditional to digital practices owing to the rapid development of "artificial intelligence" (AI) technology in healthcare systems. The dental curriculum lacks the integration of emerging technologies such as AI, which could prepare students for the evolving demands of modern dental practice. This study aimed to assess dental faculty members' knowledge, awareness, and attitudes toward AI and provide consensus-based recommendations for increasing the adoption of AI in dental education and dental practice.

Method: This mixed-method study was conducted via a modified version of the General Attitudes toward Artificial Intelligence Scale (GAAIS) and Focus Group Discussions (FGD). Four hundred faculty members from both public and private dental colleges in Pakistan participated. The quantitative data were analyzed using SPSS version 23. Otter.ai was used to transcribe the data, followed by thematic analysis to generate codes, themes, and subthemes.

Results: The majority of the faculty members was aware of the application of AI in daily life and learned about AI mainly from their colleagues and social media. Fewer than 20% of faculty members were aware of terms such as machine learning and deep learning. 81% of the participants acknowledged the need for and limited opportunities to learn about AI. Overall, the dental faculty demonstrated a generally positive attitude toward AI, with a mean score of 3.5 (SD ± 0.61). The benefits of AI in dentistry, the role of AI in dental education and research, and barriers to AI adoption and recommendations for AI integration in dentistry were the main themes identified from the FGD.

Conclusions: The dental faculty members showed general awareness and positive attitudes toward AI; however, their knowledge regarding advanced AI concepts such as machine learning and deep learning was limited. The major barriers identified in AI adoption are financial constraints, a lack of AI training, and ethical concerns for data management and academics. There is a need for targeted education initiatives, interdisciplinary and multi-institutional collaborations, the promotion of local manufacturing of such technologies, and robust policy initiatives by the governing body.