Educational gaps and factors associated with artificial intelligence adoption among Egyptian periodontists: a...

In periodontology, Artificial Intelligence (AI) applications, ranging from radiographic evaluation to outcome prediction, are emerging. However, their adoption is impacted by practitioners' awareness, attitudes, and perceived barriers. Evidence regarding AI adoption among Egyptian periodontists remains limited. Therefore, this study aimed to assess knowledge, perceptions, usage, and concerns regarding AI among Egyptian periodontists, and to identify demographic and professional factors associated with AI adoption. A multicenter cross-sectional online survey using a 33-item questionnaire was uploaded via Google Forms and distributed to eligible Egyptian periodontists. To gather information about participants' knowledge, opinions, and concerns about artificial intelligence in periodontology, the survey used closed-ended questions with a 3-point Likert-type scale. A total of 275 Egyptian periodontists took part. Although familiarity with AI was high (98.2%), only 31.3% reported understanding its working principles. Attitudes toward AI were generally positive, with 89.8% considering it a new era, and 80.7% expecting it to significantly improve periodontology. Despite substantial interest, practical familiarity with AI-based dental software remained limited (10.9%), with research being the most common application area (58.2%), followed by implant planning (13.1%) and diagnosis (12.7%). The main concerns centered on over-reliance on AI affecting critical thinking skills (68.4%), the reliability of AI-assisted periodontal diagnosis (65.8%), security risks (59.6%), and patient privacy issues (53.1%). Ordinal logistic regression analyses identified several factors significantly associated with AI-related outcomes, with AI working principle knowledge associated with age and professional experience. Male gender was significantly associated with the perception that AI could replace periodontists. AI-related educational engagement was associated with age, professional experience, and institutional affiliation. AI-related concerns were associated with gender and educational level. The findings suggest a gap between high awareness and limited adoption of AI among Egyptian periodontists despite generally positive attitudes. Key demographic and professional variables such as age, experience, gender, and institutional affiliation emerged as significant associated factors in the regression models for knowledge, perception, usage, and concerns. The study results highlight potential educational gaps and support the integration of AI education in postgraduate programs.