Arti fi cial Intelligence in Endodontic Education

Aims: The future dental and endodontic education must adapt to the current digitalized healthcare system in a hyper-connected world. The purpose of this scoping review was to investigate the ways an endodontic education curriculum could bene fit from the implementation of arti ficial intelligence (AI) and overcome the limitations of this technology in the delivery of healthcare to patients. Methods: An electronic search was carried out up to December 2023 using MEDLINE, Web of Science, Cochrane Library, and a manual search of reference literature. Grey literature, ongoing clinical trials were also searched using ClinicalTrials.gov. Results: The search identi fied 251 records, of which 35 were deemed relevant to arti ficial intelligence (AI) and Endodontic education. Areas in which AI might aid students with their didactic and clinical endodontic education were identi fied as follows: 1) radiographic interpretation; 2) differential diagnosis; 3) treatment planning and decisionmaking; 4) case dif ficulty assessment; 5) preclinical training; 6) advanced clinical simulation and case-based training, 7) real-time clinical guidance; 8) autonomous systems and robotics; 9) progress evaluation and personalized education; 10) calibration and standardization. Conclusions: AI in endodontic education will support clinical and didactic teaching through individualized feedback; enhanced, augmented, and virtually generated training aids; automated detection and diagnosis; treatment planning and decision support; and AI-based student progress evaluation, and personalized education. Its implementation will inarguably change the current concept of teaching Endodontics. Dental educators would bene fit from introducing AI in clinical and didactic pedagogy; however, they must be aware of AI 's limitations and challenges to overcome. (J Endod 2024;50:562 - 578.)