{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T13:06:07Z","timestamp":1784034367054,"version":"3.55.0"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Dent. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The growing bases of Artificial Intelligence (AI) applications ranging from diagnostic support to immersive training have rapidly advanced in the dental education field. Endodontics, by its very nature of relying so highly on a proper diagnosis and careful technical execution, is an indication through which AI may be best poised to succeed the most in specialty care.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The aim of this systematic review was to assess the role of artificial intelligence (AI): machine learning (ML), deep learning (DL), virtual\/augmented reality (VR\/AR) and large language models (LLMs) related to endodontic education based on available evidence published until September 2025.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This review was performed in accordance with the PRISMA 2020 guidelines. Publication databases were reviewed included PubMed, Scopus, Web of Science and Cochrane. Inclusion Criteria: Studies that evaluated any form of AI for didactic, preclinical or clinical education in endodontics and\/or patient-centered education were included. Study characteristics, AI domains, applications and outcomes were extracted. Risk of bias and methodological quality were evaluated according to study design using RoB 2, ROBINS-I, AXIS, and AMSTAR-2 tools.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Fifteen studies were included. Radiographic interpretation augmented by AI improved sensitivity and specificity to reduce false positive reporting especially for junior clinicians. In preclinical training, VR\/AR simulations have shown to improve psychomotor skills, confidence and knowledge acquisition. LLMs can be useful in producing exam questions and case-based Q&amp;amp;A, although the accuracy and discriminatory ability varied. AI mediated Patient education interventions led to anxiety reduction and comprehension. There was heterogeneity of outcome measures, dataset bias; reliability and transparency issues.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI holds promise for use in diagnostic, didactic and preclinical endodontic education. They must be safely implemented in a controlled format, under the supervision of faculty and with objective evaluation metrics in place.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical significance<\/jats:title>\n                    <jats:p>AI provides quantifiable benefits in endodontic education by improving accuracy of diagnosis, assisting decision-making and facilitating dental students training using VR\/AR simulation. Some interventions using AI in curricula may allow the student to acquire skills faster, feel more confident, and transfer these benefits to improved patient communication. But we need to make sure our integration is backed up with faculty monitoring, transparent AI models and rigorous validation before putting it in any production environment or relying on it too heavily for exam outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdmed.2026.1844279","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T12:20:54Z","timestamp":1784031654000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Applications of artificial intelligence in endodontic education: a systematic review"],"prefix":"10.3389","volume":"7","author":[{"given":"Mohammed","family":"Mustafa","sequence":"first","affiliation":[{"name":"Department of Conservative Dental Sciences, College of Dentistry, Prince Sattam Bin Abdulaziz University","place":["Al-Kharj, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed A.","family":"Almokhatieb","sequence":"additional","affiliation":[{"name":"Department of Conservative Dental Sciences, College of Dentistry, Prince Sattam Bin Abdulaziz University","place":["Al-Kharj, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulaziz","family":"Abdulwahed","sequence":"additional","affiliation":[{"name":"Department of Conservative Dental Sciences, College of Dentistry, Prince Sattam Bin Abdulaziz University","place":["Al-Kharj, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laila S.","family":"Almufleh","sequence":"additional","affiliation":[{"name":"Department of Conservative Dental Sciences, College of Dentistry, Prince Sattam Bin Abdulaziz University","place":["Al-Kharj, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahad","family":"Albader","sequence":"additional","affiliation":[{"name":"Department of Conservative Dental Sciences, College of Dentistry, Prince 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