ARTIFICIAL INTELLIGENCE IN DENTAL EDUCATION

Abstract

AIM: Artificial intelligence (AI) has rapidly gained a foothold in clinical dentistry and has made significant contributions across various domains. In addition to the benefits it brings to clinical practice, AI applications and AI-supported software in dental education remain an area with potential for further development and promise. The integration of AI into dental education is a contemporary and innovative approach that provides benefits for both educators and students. AI-based approaches, which have the potential to transform traditional educational models in dental schools, are introducing intelligent teaching systems to enhance learning quality and support student success. This has the potential to lead to a reassessment and even profound changes in the learning, teaching, assessment, and feedback processes in dental education. METHODS: This review, prepared through a traditional approach, examines the current state and potential impacts of AI applications in dental education. With the rapid advancement of AI, there has been an increase in publications regarding its widespread use in medical education. Innovations brought by AI are highlighted in pre-graduation education, curriculum content, assessment, creation of 3D virtual learning environments, and future perspectives in dental education. The role of AI in dental education has been shared with examples from the literature, focusing on educators, students, and educational systems. RESULTS: The use of AI in medical education is continuously expanding, undergoing constant transformation for effective theoretical and practical training in the healthcare field. A range of innovative advancements, from AI-supported applications and software to virtual reality simulators, haptic devices, and robotic patients, are rapidly being integrated into the challenging pre-clinical and clinical education processes of dental education. These technologies enable students to reach the expected motor skill level more quickly and allow them to engage in activities that simulate real patient experiences during the pre-clinical phase. In the clinical phase, AI-supported clinical decision support systems help reduce clinical errors, facilitate safe dental practices, assist in analyzing patient findings, and aid in treatment planning, ultimately improving treatment quality. The use of these technologies in education, curriculum development, and assessment processes significantly contributes to the advancement of dental education for both educators and students. The acceptance of these technologies by both students and educators is another key factor that enhances the effectiveness of AI in educational processes. CONCLUSIONS: For dental students and educators to become effective users of AI-supported applications, it is essential that they play a significant role in the education of both their peers and patients. In particular, it is crucial for students to be well-trained in face-to-face patient management, especially when using AI-based technologies. In this context, the use of AI applications, their role in dental education, their advantages and disadvantages, and their limitations have been discussed. The active use of AI in dental education provides an innovative, student-centered approach to learning. The integration of AI into education will not only advance dental education but also ensure the effective use of innovative technologies in clinical applications in lifelong learning.

Keywords

• dentistry
• Competency
• Curriculum

DİŞ HEKİMLİĞİ EĞİTİMİNDE YAPAY ZEKA

Abstract

Amaç: Yapay zekâ, diş hekimliği alanında klinik uygulamalarda hızla kendine yer bulmuş ve çeşitli alanlarda önemli katkılar sağlamıştır. Klinik kullanımının getirdiği faydaların yanı sıra diş hekimliği eğitimi bağlamında otomatik öğrenme sistemleriyle desteklenen ve yapay zeka tabanlı yazılımlar, halen gelişime açık ve potansiyel vaat eden bir alandır. Diş hekimliği eğitimine yapay zekanın entegrasyonu eğitici ve öğrenciler açısından faydalar sağlayan güncel ve inovatif bir yaklaşımdır. Diş hekimliği fakültelerinde geleneksel eğitim modellerini dönüştürme potansiyeline sahip olan yapay zekâ tabanlı yaklaşımlar, öğrenme kalitesini artırmak ve öğrenci başarısını desteklemek amacıyla zeki öğretim sistemlerini devreye sokmaktadır. Bu durum, diş hekimliği eğitiminde öğrenme süreçleri, öğretme, ölçme-değerlendirme ve geri bildirim süreçlerinin gözden geçirilmesine ve hatta köklü değişimlere kapı aralayabilecek bir potansiyele sahiptir. Yöntem: Bu derleme, geleneksel bir yöntemle hazırlanmış olup, diş hekimliği eğitiminde yapay zekâ uygulamalarının mevcut durumu ve potansiyel etkilerini incelemektedir. Son zamanlarda yapay zekanın hızla gelişmesiyle birlikte literatürde tıp eğitimi alanında da yaygın kullanımına ilişkin yayınlar artmaktadır. Mezuniyet öncesi eğitim öğretimde, müfredat içeriğinde, ölçme değerlendirmede, üç boyutlu sanal eğitim ortamları yaratılmasında ve diş hekimliği eğitiminin gelecek perspektifleri açısından yapay zekanın getirdiği yenilikler vurgulanmıştır. Yapay zekanın diş hekimliği eğitimindeki yeri eğiticiler, öğrenciler ve eğitim sistemleri açısından literatür örnekleriyle paylaşılmıştır. Bulgular: Tıp eğitiminde yapay zekâ kullanımı, sağlık alanında etkin teorik ve pratik eğitim açısından sürekli bir dönüşüm geçirerek kapsamını genişletmektedir. Yapay zekâ destekli uygulama ve yazılımlar ile sanal gerçeklik simülatörlerinden haptik cihazlara, robotik hastalara kadar pek çok inovatif yenilik, diş hekimliği eğitiminin zorlu klinik öncesi ve klinik eğitim süreçlerine hızla entegre olmaktadır. Bu teknolojiler, öğrencilerin beklenen motor beceri seviyesine daha kısa sürede ulaşmalarını sağlamakta ve klinik öncesi dönemde gerçek hasta deneyimine benzer çalışmalar yapmalarına olanak tanımaktadır. Klinik dönemde ise yapay zekâ tabanlı sistemler klinik hataları azaltarak güvenli dental uygulamalar yapılmasına, hasta bulgularının analizinde, tedavi planlamasında karar vermede yardımcı olmakta böylece tedavi kalitesini artırmaktadır. Bu teknolojilerin eğitim, müfredat geliştirilmesi, ölçme değerlendirilmesi gibi süreçlerde kullanımı, hem eğiticiler hem de öğrenciler açısından diş hekimliği eğitiminin ilerlemesine önemli katkılar sunmaktadır. Öğrencilerin ve eğiticilerin bu teknolojileri kabul edilebilir bulması, eğitim süreçlerinde yapay zekânın etkinliğini artıran başka bir önemli faktördür. Sonuç: Diş hekimliği öğrencilerinin ve eğiticilerin yapay zekâ destekli uygulamalar konusunda etkin birer kullanıcı olmaları, hem meslektaşlarının hem de hastalarının eğitimi konusunda önemli bir rol oynamalarını gerektirmektedir. Özellikle yapay zekâ tabanlı teknolojilerin kullanıldığı durumlarda, öğrencilerin, hasta yönetiminde yüz yüze deneyim kazanmaları oldukça önemli bir faktördür. Yapay zeka tabanlı uygulamaların kullanıldığı durumlar, yapay zekanın diş hekimliği eğitimindeki yeri, avantaj ve dezavantajları, kısıtlılıkları tartışılmıştır. Yapay zekanın diş hekimliği eğitiminde aktif kullanılması, öğrenci merkezli öğrenmeye yönelik olarak yenilikçi bir yaklaşım sağlamaktadır. Eğitime yapay zekanın entegrasyonu hem diş hekimliğinde mezuniyet öncesi eğitiminde hem de yaşam boyu öğrenmede gelecekte klinik uygulamalarda inovatif teknolojilerin etkin bir şekilde kullanılmasını sağlayacaktır.

Keywords

• diş hekimliği
• müfredat
• Yeterlik

References

1. 1. Deshmukh S. Artificial intelligence in dentistry. J Int Clin Dent Res Organ. 2018;10:47.
2. 2. Abonamah A, Tariq M, Shilbayeh S. On the Commoditization of Artificial Intelligence. Front. Psychol. 2021;12: 696346.
3. 3. Goralski M, Tan T. Artificial intelligence and sustainable development. Int J Manag Educ. 2020;18(1): 100330
4. 4. Sogani J, Allen Jr B, Dreyer K, McGinty G. Artificial intelligence in radiology: the ecosystem essential to improving patient care. Clin. Imaging. 2020;59(1): A3-A6.
5. 5. Schwendicke F, Samek W, Krois J. Artificial Intelligence in Dentistry: Chances and Challenges. Journal of Dental Research. 2020;99(7):769-774.
6. 6. Hornik K. 1991. Approximation capabilities of multilayer feedforward networks. Neural Netw. 4(2):251–257.
7. 7. Chowdhary KR. Natural Language Processing. In: Fundamentals of Artificial Intelligence. Springer, New Delhi. 2020. https://doi.org/10.1007/978-81-322-3972-7_19
8. 8. Yin RK, and Moore GB. The use of advanced technologies in special education: Prospects from robotics, artificial intelligence, and computer simulation. Journal of Learning Disabilities, 1987;20(1):60-63.

9. 9. Aminoshariae A, Kulild J, Nagendrababu V. Artificial intelligence in endodontics: Current applications and future directions. J Endod. 2021;47:1352–1357.
10. 10. Bostrom N, Yudkowsky E. The ethics of artificial intelligence. In Artificial intelligence safety and security. Chapman and Hall/CRC. 2018;57-69.
11. 11. Khanagar SB, Naik S, Al Kheraif AA, et al. Application and performance of artificial intelligence technology in oral cancer diagnosis and prediction of prognosis: A systematic review. Diagnostics (Basel) 2021;11.
12. 12. Chartrand G, Cheng P, Vorontsov E et al. Deep learning: a primer for radiologists. Radiographics. 2017;37(7):2113-31.
13. 13. Soori M, Arezoo B, Dastres R. Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics. 2023;3:54-70.
14. 14. Khanna S, Dhaimade P. Artificial intelligence: transforming dentistry today. Indian J Basic Appl Med Res. 2017;6(3):161-7.
15. 15. LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015;521(7553):436-44.
16. 16. Agrawal P, Nikhade P. Artificial intelligence in dentistry: past, present, and future. Cureus. 2022;14(7):e27405.
17. 17. Chen YW, Stanley K, Att W. Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int. 2020;51(3):248-57.
18. 18. Perry S, Bridges S M, Burrow M F. A review of the use of simulation in dental education. Simul Healthc. 2015;10:31-37.
19. 19. Harte M, Carey B, Feng Q et al. Transforming undergraduate dental education: the impact of artificial intelligence. Br Dent J. 2025;238:57–60.
20. 20. Li Y, Ye H, Ye F et al. The Current Situation and Future Prospects of Simulators in Dental Education. J Med Internet Res. 2021 Apr 8;23(4):e23635.
21. 21. Roy E, Bakr M M, George R. The need for virtual reality simulators in dental education: a review. Saudi Dent J. 2017;29:41-47.
22. 22. Islam NM, Laughter L, Sadid-Zadeh R, Smith C, Dolan TA, Crain G, Squarize CH. Adopting artificial intelligence in dental education: a model for academic leadership and innovation. J Dent Educ. 2022;86(11):1545–51.
23. 23. Gandedkar NH, Wong MT, Darendeliler MA. Role of virtual reality (VR), augmented reality (AR) and artificial intelligence (AI) in tertiary education and research of orthodontics: an insight. Semin Orthod. 2021;27(2):69–77.
24. 24. Luckin R, Holmes W, Griffiths M, Forcier LB. Intelligence unleashed: an argument for AI in education. London: Pearson Education; 2016.
25. 25. Ifenthaler D, Yau JY-K. Higher education stakeholders’ views on learning analytics policy recommendations for supporting study success. Int J Learn Anal Artific Intell Educ. 2019;1:28–42.
26. 26. Van der Hoeven D, Zhu L, Busaidy K, Quock R L, Holland J N, van der Hoeven R. Integration of basic and clinical sciences: student perceptions. Med Sci Educ. 2019; 30: 243-52.
27. 27. Zhai X, Chu X, Chai CS, Jong MSY, Istenic A, Spector M, Li Y. A Review of Artificial Intelligence (AI) in Education from 2010 to 2020. Complexity. 2021(1);8812542.
28. 28. Pedró F, Subosa M, Rivas A, Valverde P. Artificial intelligence in education: challenges and opportunities for sustainable development, UNESCO working papers on education policy. 2019.
29. 29. Gonzalez MAG, Abu Kasim NH, Naimie Z. Soft skills and dental education. European Journal of Dental Education. 2013;17(2):73-82.
30. 30. Sharma D, Malik G, Koshy G, Sharma V. Artificial intelligence: need to reboot dental education. Univ J Dent Sci. 2021;7(2):138–42.
31. 31. Zheng L, He Z, Wei D, Keloth V, Fan JW, Lindemann L, Zhu X, Cimino JJ, Perl Y. A review of auditing techniques for the unified medical language system. J Am Med Inform Assoc. 2020;27(10):1625–38
32. 32. González-Calatayud V, Prendes-Espinosa P, Roig-Vila R. Artificial intelligence for student assessment: a systematic review. Appl Sci. 2021;11(12):5467.
33. 33. Tadinada A, Gul G, Godwin L, Al Sakka Y, Crain G, Stanford CM, Johnson J. Utilizing an organizational development framework as a road map for creating a technology-driven agile curriculum in predoctoral dental education. J Dent Educ. 2023;87(3):394–400.
34. 34. Dzyuba N, Jandu J, Yates J, Kushnerev E. Virtual and augmented reality in dental education: the good, the bad and the better. Eur J Dent Educ. 2022;Nov 6.
35. 35. Pulijala Y, Ma M, Pears M, Peebles D, Ayoub A. Effectiveness of immersive virtual reality in surgical training—a randomized control trial. J Oral Maxillofac Surg. 2018;76:1065-1072.
36. 36. Le Blanc VR, Urbankova A, Hadavi F, Lichtenthal RM. A Preliminery Study in Using Virtual Reality to Train Dental Students. J Dent Educ. 2004;68(3):378-83.
37. 37. Mladenovic R, Dakovic D, Pereira L, Matvijenko V, Mladenovic K. Effect of augmented reality simulation on administration of local anaesthesia in paediatric patients. Eur J Dent Educ. 2020;24:507-512.
38. 38. Dolega-Dolegowski D, Dolega-Dolegowska M, Pregowska A, Malinowski K, Proniewska K. The application of mixed reality in root canal treatment. Applied Sciences. 2023;13(7):4078.
39. 39. Diegritz C, Fotiadou C, Fleischer F, Reymus M. Tooth Anatomy Inspector: A comprehensive assessment of an extended reality (XR) application designed for teaching and learning of root canal anatomy by students. International Endodontic Journal. 2024;57(11):1682-1688.
40. 40. Basmaci F, Bulut AC, Ozcelik E, Zerdali Ekici S, Kilicarslan MA, Cagiltay NE. Evaluation of the effects of avatar on learning temporomandibular joint in a metaverse‐based training. J Dent Educ. 2024;Dec17.
41. 41. Coşkun S, Güngör M. A comparative study of use of artificial intelligence in oral radiology education. Eur Ann Dent Sci. 2023;50(1):41–6.
42. 42. Yüzbaşıoğlu E. Attitudes and perceptions of dental students towards artificial intelligence. J Dent Educ. 2021;85(1):60–8.
43. 43. Schwendicke F, Chaurasia A, Wiegand T, Uribe SE, Fontana M, Akota I, Tryfonos O, Krois J. IADR e-oral health network and the ITU/WHO focus group AI for health. Artificial intelligence for oral and dental healthcare: core education curriculum. J Dent. 2023;128:104363.
44. 44. Thurzo A, Strunga M, Urban R, Surovková J, Afrashtehfar KI. Impact of artificial intelligence on dental education: a review and guide for curriculum update. Educ Sci. 2023;13(2):150.
45. 45. Chen L, Chen P, Lin, Z. Artificial intelligence in education: A review. IEEE Access, 2020;8:75264 – 75278.
46. 46. Rezigalla AA. AI in medical education: uses of AI in construction type A MCQs. BMC Med Educ. 2024;24:247.
47. 47. Holmes W, Bialik M, Fadel C. Artificial intelligence in education: Promises and implications for teaching and learning. Boston: Center for Curriculum Redesign. 2019. https://curriculumredesign.org/wp-content/uploads/AIED-Book-Excerpt-CCR.pdf
48. 48. Owan V, Abang KB, Idika DO, Etta EO, Bassey BA. Exploring the potential of artificial intelligence tools in educational measurement and assessment. EURASIA J Math Sci Tech Ed. 2023;19(8).