MODERN APPROACHES TO THE APPLICATION OF DIGITAL TECHNOLOGIES AND ARTIFICIAL INTELLIGENCE IN DENTAL EDUCATION AND PRACTICE (LITERATURE REVIEW)
DOI:
https://doi.org/10.32689/2663-0672-2026-1-6Keywords:
artificial intelligence, digital technology, clinical simulation, dental innovation, medical technology, virtual realityAbstract
Background. Digital technologies and artificial intelligence are increasingly entering not only the everyday life of every person, but also medicine, in particular dental practice and medical education. Artificial intelligence technologies are developing especially rapidly, which open up new opportunities for training future dentists and facilitate the work of doctors in practice. Artificial intelligence is already used to analyze X-ray images, predict treatment outcomes and create individual dental treatment plans. The introduction of intelligent learning systems and virtual reality into dental education and the use of artificial intelligence in practice is not only a modern and relevant topic, but also a necessary condition for training future specialists in dentistry, capable of working effectively in the conditions of modern digital medicine. The purpose of the work. To analyst the possibilities and prospects of applying artificial intelligence in the training system of dental students, as well as to evaluate their impact on enhancing the effectiveness of clinical practice among dentists within the context of digital transformation in healthcare. Materials and methods. In the course of the study, scientific developments published on the research platforms Google Scholar and PubMed were analyzed. Semantic analysis of sources were used. The methodological basis of the study was a systematic approach. Results and discussions. Analysis of available systematic reviews suggests that the use of artificial intelligence in the education of dental students increases the quality of education, and use in clinical practice improves diagnostic accuracy, contributing to a personalized approach and more effective treatment of patients. Conclusions. The use of artificial intelligence in the work of a dentist helps to improve diagnostics and planning, as it allows for automatic analysis of diagnostic images and the creation of personalized treatment plans. The use of digital protocols and automated technologies allows for the development of personalized treatment plans, which increases their effectiveness. Despite the advantages, the successful application of such technologies requires careful validation of models, adaptation to individual clinical needs, and consideration of ethical and legal aspects.
References
Algarni, Y.A., Saini, R.S., Vaddamanu, S.K., Quadri, S.A., Gurumurthy, V., Vyas, R., Baba, S.M., Avetisyan, A., Mosaddad, S.A., & Heboyan, A. (2024). The impact of virtual reality simulation on dental education: A systematic review of learning outcomes and student engagement. Journal of dental education, 88, 1–14.
Amisha, Malik, P., Pathania, M., Rathaur, V.K.. (2019). Overview of artificial intelligence in medicine. Journal of Family Medicine and Primary Care, 8(7), 2328–2331.
Arik, S.O., Ibragimov, B., Xing, L. (2017). Fully automated quantitative cephalometry using convolutional neural networks. Journal of Medical Imaging, 4(1), 014501.
Badawy, M.K., Carrion, D., Mahesh, M. (2025). Medical physicists at the forefront of multidisciplinary AI integration in healthcare. Phys Med, 135, 105007.
Baniasadi, T., Ayyoubzadeh, S.M., Mohammadzadeh, N. (2020). Challenges and Practical Considerations in Applying Virtual Reality in Medical Education and Treatment. Oman Medical Journal, 35(3), e125.
Claman, D., Sezgin, E. (2024). Artificial Intelligence in Dental Education: Opportunities and Challenges of Large Language Models and Multimodal Foundation Models. JMIR Medical Education, 10, e52346.
CranioCatch. (n.d.). AI in Dentistry Education. Доступно на: https://www.craniocatch.com/en/blogs/ai-dentistry-education (Активне посилання на період 15.03.2026).
Dunkel, L., Fernandez-Luque, L., Loche, S., Savage, M.O. (2021). Digital technologies to improve the precision of paediatric growth disorder diagnosis and management. Growth Hormone & IGF Research, 59, 101408.
Elgarba, B.M., Fontenele, R.C., Tarce, M., Jacobs, R. (2024). Artificial intelligence serving pre-surgical digital implant planning: A scoping review. Journal of dentistry, 143, 104862.
Esteva, A., Robicquet, A., Ramsundar, B. et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29.
Gao, S., Wang, X., Xia, Z., Zhang, H., Yu, J., Yang, F. (2025). Artificial Intelligence in Dentistry: A Narrative Review of Diagnostic and Therapeutic Applications. Med Sci Monit, 8(31), e946676.
Goumballa, N., de-Oliveira, F., Frandon, J., Coisy, F., Goupil, J., Longueville, F., Daladouire, C., Grandpierre, R.G., Beregi, J.P. (2025). Trends in radiology requests and emergency admissions: a 10-year retrospective study in a university hospital. J Epidemiol Popul Health, 73(3), 203108.
Gracea, R.S., Winderickx, N., Vanheers, M., Hendrickx, J., Preda, F., Shujaat, S., Cadenas de Llano-Pérula, M., Jacobs R. (2025). Artificial intelligence for orthodontic diagnosis and treatment planning: A scoping review. Journal of dentistry, 152, 105442.
Hung, M., Voss, M.W., Rosales, R.M. et al. (2019). Application of machine learning for diagnostic prediction in dental care: A systematic review. PLOS ONE, 16(5), e0251521.
IBM Corporation. (2024). IBM Think. Artificial Intelligence in Medicine. https://www.ibm.com/think/topics/artificial-intelligence-medicine (Активне посилання на період 15.03.2026).
Iosif, L., Țâncu, A.M., Amza, O.E., Gheorghe, G.F., Dimitriu, B., Imre, M. (2024). Artificial intelligence in prosthetic dentistry: A narrative review Bridging Established Knowledge and Innovation Gaps Across Regions and Emerging Frontiers. Prosthesis, 6(6), 1281–1299.
Jaskari, J., Sahlsten, J., Järnstedt, J. et al. (2020). Deep Learning Method for Mandibular Canal Segmentation in Dental Cone Beam Computed Tomography Volumes. Scientific reports, 10(1), 5842.
Joda, T., Waltimo, T., Probst-Hensch, N., Pauli-Magnus, C., Zitzmann, N.U. (2019). Health Data in Dentistry: An Attempt to Master the Digital Challenge. Public Health Genomics, 22(1–2), 1–7.
Khanagar, S.B., Al-Ehaideb, A., Maganur, P.C., Vishwanathaiah, S., Patil, S., Baeshen, H.A., Sarode, S.C., Bhandi, S. (2021). Developments, application, and performance of artificial intelligence in dentistry – A systematic review. Journal of dental sciences, 16(1), 508–522.
Kunz, F., Stellzig-Eisenhauer, A., Zeman, F., Boldt, J. (2020). Artificial intelligence in orthodontics : Evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. Journal of orofacial orthopedics, 81(1), 52-68.
Lampropoulos, G., Kinshuk. (2024). Virtual reality and gamification in education: a systematic review. Education Tech Research Dev, 72, 1691–1785.
Lee J.H., Kim D.H., Jeong S.N., Choi S.H. (2018). Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of Dentistry, 77, 106–111.
Lee, S.J., Poon, J., Jindarojanakul, A., Huang, C.C., Viera, O., Cheong, C. W., Lee, D. (2025). Artificial intelligence in dentistry: Exploring emerging applications and future prospects. Journal of Dentistry, 155, 105648.
Mangano, F.G., Admakin, O., Lerner, H., Mangano, C. (2023). Artificial intelligence and augmented reality for guided implant surgery planning: A proof of concept. Journal of dentistry, 133, 104485.
Mangano, F.G., Veronesi, G., Hauschild, U., et al. (2016). Trueness and precision of four intraoral scanners in oral implantology: A comparative in vitro study. PLOS ONE, 11(9), e0163107.
Mansoory, M., Azizi, S.M., Mirhosseini F. et al. (2022). A study to investigate the effectiveness of the application of virtual reality technology in dental education. BMC Med Educ, 22, 457.
Maspero, C., Abate, A., Cavagnetto, D., El Morsi, M., Fama, A., Farronato, M. (2020). Available Technologies, Applications and Benefits of Teleorthodontics. A Literature Review and Possible Applications during the COVID-19 Pandemic. Journal of Clinical Medicine, 9(6), 1891.
Mohammad-Rahimi, H., Motamedian, S.R., Rohban, M.H., Krois, J., Uribe, S.E., Mahmoudinia, E., Rokhshad, R., Nadimi, M., Schwendicke, F. (2022). Deep learning for caries detection: A systematic review. Journal of dentistry, 122, 104115.
Monill-González, A., Rovira-Calatayud, L., d’Oliveira, N.G., Ustrell-Torrent, J.M. (2021). Artificial intelligence in orthodontics: Where are we now? A scoping review. Orthodontics & craniofacial research, 24(2), 6–15.
Pauwels, R. (2026). Artificial Intelligence in Dental Diagnostics and Treatment Planning: General Principles, Current State, and Future Perspectives. Aktuel Nordisk Odontologi, 51(1), 7–23.
Rajan, R.S., Kumar, H.S., Sekhar, A., Nadakkavukaran, D., Feroz, S.M., Gangadharappa, P. (2024). Evaluating the Role of AI in Predicting the Success of Dental Implants Based on Preoperative CBCT Images: A Randomized Controlled Trial. Journal of pharmacy & bioallied sciences, 16(1), 886–888.
Ramesh, A.N., Kambhampati, C., Monson, J.R., Drew, P.J. (2004). Artificial intelligence in medicine. Annals of the Royal College of Surgeons of England, 86(5), 334–8.
Recommendation on the Ethics of Artificial Intelligence. (2021). UNESCO. Доступно на: https://unes-doc.unesco.org/ark:/48223/pf0000380455 (Активне посилання на період 15.03.2026).
Saenko, M. S. (2022). Shtuchnyi intelekt: sutnist, suchasnyi stan rozvytku ta mozhlyvosti yoho zastosuvannia u medytsyni. Develompent of natural sciences as a basis of new achievements in medicine, 270–275.
Satapathy, S. K., Kunam, A., Rashme, R., Sudarsanam, P.P., Gupta, A., Kumar, H. S. K. (2024). AI-Assisted Treatment Planning for Dental Implant Placement: Clinical vs AI-Generated Plans. Journal of pharmacy & bioallied sciences, 16(1), 939–941.
Schwendicke, F., Samek, W., Krois J. (2020). Artificial intelligence in dentistry: Chances and challenges. Journal of Dental Research, 99(7), 769–774.
Siddarthan, I.J., Huang, C., Kumar, P., Rubin, J.E., White, R.S., Mehta, N., Jotwani, R. (2025). Virtual Reality for Pre-Procedural Planning of Interventional Pain Procedures: A Real-World Application Case Series. J Clin Med, 14(9), 3019.
Simodont Dental Trainer. (n.d.). Доступно на: https://www.simodontdentaltrainer.com/ (Активне посилання на період 15.03.2026).
SIMtoCARE Dente. (n.d.). Simtocare – Dental Simulation Solutions. Доступно на: https://www.simtocare.com (Активне посилання на період 15.03.2026).
Thurzo, A., Strunga, M., Urban, R., Surovková, J., Afrashtehfar, K.I. (2023). Impact of Artificial Intelligence on Dental Education: A Review and Guide for Curriculum Update. Education Sciences, 13(2), 150.
Topol, E.J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25, 44–56.
Virteasy Dental. (n.d.). HRV Simulation. Доступно на: https://virteasy.com/virteasy-dental (Активне посилання на період 15.03.2026).
Waller M., Stotler C. (2018). Telemedicine: a Primer. Current Allergy and Asthma Reports, 18(10), 54.









