Educational aspects of artificial intelligence in oral and maxillofacial radiology: insights from a scoping review

Abstract

To gather evidence from literature on Artificial Intelligence applications in educational settings and assess the educational elements involved in Oral and Maxillofacial Radiology. A systematic scoping review was performed using predefined keywords and truncation search strategies across PubMed, Scopus, and Web of Science, focusing on artificial intelligence, oral and maxillofacial radiology, and dental education. The initial search retrieved 3,124 articles published from 2014 to November 2024 were screened. Of these, only 8 articles met the criteria, specifically focusing on educational aspects within Oral and Maxillofacial Radiology; the others were excluded mainly because they focused on general dentistry. Three studies focus on Knowledge, Attitude, and Perception of dental students and educators, while others involve clinical applications among students, Generative Adversarial Networks for radiographic image generation, and large language models (LLMs) for theoretical OMFR comparison among students and LLMs. The educational elements identified include utilizing AI as a learning tool, incorporating AI knowledge and fundamentals into lectures, and evaluating AI’s performance in theoretical and clinical OMFR. Prospective research on the educational aspects of Artificial Intelligence (AI) integration in Oral and Maxillofacial Radiology appears promising, given the current limited evidence base, which tends to emphasize clinical implications more than direct educational value. Additionally, strategically incorporating AI into pedagogical frameworks can help develop dental professionals with improved technological literacy, a strong understanding, and greater confidence in using AI technologies.

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