Artificial Intelligence and Dentomaxillofacial Radiology Education: Innovations and Perspectives

Abstract

Artificial intelligence (AI) is transforming dentomaxillofacial radiology education by enabling adaptive, personalized, and data-driven learning experiences. This review critically examines the pedagogical potential of AI within dental curricula, focusing on its ability to enhance student engagement, improve diagnostic competencies, and streamline clinical decision-making processes. Key innovations include real-time feedback systems, AI-guided simulations, automated assessments, and clinical decision support tools. Through these resources, AI transforms static learning into dynamic, interactive, and competency-based education. Additionally, this review discusses the integration of AI into formative assessment frameworks, such as OSCEs and mini-CEX, and its impact on student confidence, performance tracking, and educational scalability. Although primarily narrative in structure, this review synthesizes the current literature on dentomaxillofacial radiology education, supported by selected insights from medical radiology, to provide a comprehensive and up-to-date perspective on the educational applications of AI. Challenges (including ethical implications and other practical considerations) are addressed, alongside future directions for research and curriculum development. Overall, AI has the potential to significantly enhance radiology education by fostering clinically competent, ethically grounded, and technologically literate dental professionals.

Keywords: adaptive learning; artificial intelligence; clinical decision support; dental education; dentomaxillofacial radiology.

Figure 1

Schematic representation of an AI-driven feedback loop in dentomaxillofacial radiology education. AI algorithms assist students in performing diagnostic tasks such as caries detection, endodontic evaluation, pathology identification, implant and surgical planning, TMJ analysis, and anatomical interpretation. The system provides real-time and performance-based feedback, while educators facilitate critical appraisal and discussion of AI outputs. This dynamic, student-centered model promotes diagnostic accuracy through iterative learning, guided analysis, and continuous refinement of both student competence and algorithmic performance.