Pedodonti Eğitiminde Yapay Zeka Destekli Modern Yaklaşımlar
Özet
Referanslar
Turing A. Intelligent machinery (1948). B. Jack Copeland. 2004. 395 p.
Zheng L, Wang H, Mei L, Chen Q, Zhang Y, Zhang H. Artificial intelligence in digital cariology: a new tool for the diagnosis of deep caries and pulpitis using convolutional neural networks. Annals of Translational Medicine. 2021;9(9):763. doi:10.21037/atm-21-119.
Ammar N, Kühnisch J. Diagnostic performance of artificial intelligence-aided caries detection on bitewing radiographs: a systematic review and meta-analysis. Japanese Dental Science Review. 2024;60:128–36. doi:10.1016/j.jdsr.2024.02.001.
Ding H, Wu J, Zhao W, Matinlinna JP, Burrow MF, Tsoi JKH. Artificial intelligence in dentistry-A review. Frontiers in Dental Medicine. 2023;4:1085251. doi:10.3389/fdmed.2023.1085251.
Slashcheva LD, Schroeder K, Heaton LJ, Cheung HJ, Prosa B, Ferrian N, et al. Artificial intelligence-produced radiographic enhancements in dental clinical care: provider and patient perspectives. Frontiers in Oral Health. 2025;6:1473877. doi:10.3389/froh.2025.1473877
Trusculescu LM, Pitic DE, Sălcudean A, Popovici RA, Forna N, Badoiu SC, et al. Virtual Reality as a Non-Pharmacological Aid for Reducing Anxiety in Pediatric Dental Procedures. Children. 2025;12(7):930. doi: 10.3390/children12070930
Das M, Shahnawaz K, Raghavendra K, Kavitha R, Nagareddy B, Murugesan S. Evaluating the accuracy of AI-based software vs human interpretation in the diagnosis of dental caries using intraoral radiographs: An RCT. Journal of Pharmacy Bioallied Sciences. 2024;16(Suppl 1):S812–4. doi:10.4103/jpbs.jpbs_1029_23.
El-Hakim M, Anthonappa R, Fawzy A. Artificial intelligence in dental education: a scoping review of applications, challenges, and gaps. Dentistry Journal. 2025;13(9):384. doi:10.3390/dj13090384.
Claman D, Sezgin E. Artificial intelligence in dental education: opportunities and challenges of large Language models and multimodal foundation models. JMIR Medical Education. 2024;10(1):e52346. doi:10.2196/52346.
Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nature Reviews Molecular Cell Biology. 2022;23(1):40–55. doi: 10.1038/s41580-021-00407-0.
Adnan K, Fahimullah F, Farrukh U, Askari H, Siddiqui S, Jameel RA. AI-enabled virtual reality systems for dental education. International Journal of Health Sciences. 2023;7(S1):1378–92. doi:10.53730/ijhs.v7nS1.14350.
Rajinikanth SB, Rajkumar DSR, Rajinikanth A, Anandhapandian PA. An overview of artificial intelligence based automated diagnosis in paediatric dentistry. Frontiers in Oral Health. 2024;5:1482334. doi:10.3389/froh.2024.1482334.
Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, et al. Deep learning for the radiographic detection of periodontal bone loss. Scientific Reports. 2019;9(1):8495. doi:10.1038/s41598-019-44839-3
Hegde S, Gao J, Cox S, Nanayakkara S, Logothetis R, Vasa R. Machine learning algorithms enhance the accuracy of radiographic diagnosis of dental caries: a comparative study. Dentomaxillofacial Radiolgy. 2025;54(8):632–41. doi:10.1093/dmfr/twaf053.
Rampf S, Gehrig H, Möltner A, Fischer MR, Schwendicke F, Huth KC. Radiographical diagnostic competences of dental students using various feedback methods and integrating an artificial intelligence application—A randomized clinical trial. European Journal of Dental Education. 2024;28(4):925–37. doi:10.1111/eje.13028.
Yavsan ZS, Orhan H, Efe E, Yavsan E. Diagnosis of approximal caries in children with convolutional neural networks based detection algorithms on radiographs: A pilot study. Acta Odontologica Scandinavica. 2025;84:42599. doi:10.2340/aos.v84.42599.
Mertens S, Krois J, Cantu AG, Arsiwala LT, Schwendicke F. Artificial intelligence for caries detection: randomized trial. Journal of Dentistry. 2021;115:103849. doi:10.1016/j.jdent.2021.103849.
Mansoory MS, Azizi SM, Mirhosseini F, Yousefi D, Moradpoor H. A study to investigate the effectiveness of the application of virtual reality technology in dental education. BMC Medical Education. 2022;22(1):457. doi: 10.1186/s12909-022-03543-z
Zafar S, Siddiqi A, Yasir M, Zachar JJ. Pedagogical development in local anaesthetic training in paediatric dentistry using virtual reality simulator. European Archives of Paediatric Dentistry. 2021 Aug;22(4):667–74. doi: 10.1007/s40368-021-00604-7
Diaz-Navarro C, Armstrong R, Charnetski M, Freeman K, Koh S, Reedy G, et al. Global consensus statement on simulation-based practice in healthcare. Clinical Simulation in Nursing. 2024;93:101552. doi:10.1186/s41077-024-00288-1.
Zafar S, Lai Y, Sexton C, Siddiqi A. Virtual Reality as a novel educational tool in pre‐clinical paediatric dentistry training: Students’ perceptions. International Journal of Paediatric Dentistry. 2020;30(6):791–7. doi: 10.1111/ipd.12648.
Mladenovic R, Dakovic D, Pereira L, Matvijenko V, Mladenovic K. Effect of augmented reality simulation on administration of local anaesthesia in paediatric patients. European Journal Dental Education. 2020;24(3):507–12. doi: 10.1111/eje.12529.
Queen Mary University of London [Internet]. 2022. A nervous child patient visits the dental clinic - digital education studio. Available from: https://www.qmul.ac.uk/digital-education-studio/our-work/fmd-immersive-learning-lab/nervous-child-patient-vr/
Papadopoulos L, Pentzou AE, Louloudiadis K, Tsiatsos TK. Design and evaluation of a simulation for pediatric dentistry in virtual worlds. Journal of Medical Internet Research. 2013;15(10):e240. doi:10.2196/jmir.2651.
Huang S, Wen C, Bai X, Li S, Wang S, Wang X, et al. Exploring the application capability of ChatGPT as an instructor in skills education for dental medical students: randomized controlled trial. Journal of Medical Internet Research. 2025;27:e68538. doi:10.2196/68538.
Bryce M, Burns L, Ahmadi H, Hanks S, Nasser M, Zhou SM. Artificial intelligence in dental service provision: A rapid evidence assessment. London: General Dental Council; 2025. ISBN: 978-1-911654-18-6.
Referanslar
Turing A. Intelligent machinery (1948). B. Jack Copeland. 2004. 395 p.
Zheng L, Wang H, Mei L, Chen Q, Zhang Y, Zhang H. Artificial intelligence in digital cariology: a new tool for the diagnosis of deep caries and pulpitis using convolutional neural networks. Annals of Translational Medicine. 2021;9(9):763. doi:10.21037/atm-21-119.
Ammar N, Kühnisch J. Diagnostic performance of artificial intelligence-aided caries detection on bitewing radiographs: a systematic review and meta-analysis. Japanese Dental Science Review. 2024;60:128–36. doi:10.1016/j.jdsr.2024.02.001.
Ding H, Wu J, Zhao W, Matinlinna JP, Burrow MF, Tsoi JKH. Artificial intelligence in dentistry-A review. Frontiers in Dental Medicine. 2023;4:1085251. doi:10.3389/fdmed.2023.1085251.
Slashcheva LD, Schroeder K, Heaton LJ, Cheung HJ, Prosa B, Ferrian N, et al. Artificial intelligence-produced radiographic enhancements in dental clinical care: provider and patient perspectives. Frontiers in Oral Health. 2025;6:1473877. doi:10.3389/froh.2025.1473877
Trusculescu LM, Pitic DE, Sălcudean A, Popovici RA, Forna N, Badoiu SC, et al. Virtual Reality as a Non-Pharmacological Aid for Reducing Anxiety in Pediatric Dental Procedures. Children. 2025;12(7):930. doi: 10.3390/children12070930
Das M, Shahnawaz K, Raghavendra K, Kavitha R, Nagareddy B, Murugesan S. Evaluating the accuracy of AI-based software vs human interpretation in the diagnosis of dental caries using intraoral radiographs: An RCT. Journal of Pharmacy Bioallied Sciences. 2024;16(Suppl 1):S812–4. doi:10.4103/jpbs.jpbs_1029_23.
El-Hakim M, Anthonappa R, Fawzy A. Artificial intelligence in dental education: a scoping review of applications, challenges, and gaps. Dentistry Journal. 2025;13(9):384. doi:10.3390/dj13090384.
Claman D, Sezgin E. Artificial intelligence in dental education: opportunities and challenges of large Language models and multimodal foundation models. JMIR Medical Education. 2024;10(1):e52346. doi:10.2196/52346.
Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nature Reviews Molecular Cell Biology. 2022;23(1):40–55. doi: 10.1038/s41580-021-00407-0.
Adnan K, Fahimullah F, Farrukh U, Askari H, Siddiqui S, Jameel RA. AI-enabled virtual reality systems for dental education. International Journal of Health Sciences. 2023;7(S1):1378–92. doi:10.53730/ijhs.v7nS1.14350.
Rajinikanth SB, Rajkumar DSR, Rajinikanth A, Anandhapandian PA. An overview of artificial intelligence based automated diagnosis in paediatric dentistry. Frontiers in Oral Health. 2024;5:1482334. doi:10.3389/froh.2024.1482334.
Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, et al. Deep learning for the radiographic detection of periodontal bone loss. Scientific Reports. 2019;9(1):8495. doi:10.1038/s41598-019-44839-3
Hegde S, Gao J, Cox S, Nanayakkara S, Logothetis R, Vasa R. Machine learning algorithms enhance the accuracy of radiographic diagnosis of dental caries: a comparative study. Dentomaxillofacial Radiolgy. 2025;54(8):632–41. doi:10.1093/dmfr/twaf053.
Rampf S, Gehrig H, Möltner A, Fischer MR, Schwendicke F, Huth KC. Radiographical diagnostic competences of dental students using various feedback methods and integrating an artificial intelligence application—A randomized clinical trial. European Journal of Dental Education. 2024;28(4):925–37. doi:10.1111/eje.13028.
Yavsan ZS, Orhan H, Efe E, Yavsan E. Diagnosis of approximal caries in children with convolutional neural networks based detection algorithms on radiographs: A pilot study. Acta Odontologica Scandinavica. 2025;84:42599. doi:10.2340/aos.v84.42599.
Mertens S, Krois J, Cantu AG, Arsiwala LT, Schwendicke F. Artificial intelligence for caries detection: randomized trial. Journal of Dentistry. 2021;115:103849. doi:10.1016/j.jdent.2021.103849.
Mansoory MS, Azizi SM, Mirhosseini F, Yousefi D, Moradpoor H. A study to investigate the effectiveness of the application of virtual reality technology in dental education. BMC Medical Education. 2022;22(1):457. doi: 10.1186/s12909-022-03543-z
Zafar S, Siddiqi A, Yasir M, Zachar JJ. Pedagogical development in local anaesthetic training in paediatric dentistry using virtual reality simulator. European Archives of Paediatric Dentistry. 2021 Aug;22(4):667–74. doi: 10.1007/s40368-021-00604-7
Diaz-Navarro C, Armstrong R, Charnetski M, Freeman K, Koh S, Reedy G, et al. Global consensus statement on simulation-based practice in healthcare. Clinical Simulation in Nursing. 2024;93:101552. doi:10.1186/s41077-024-00288-1.
Zafar S, Lai Y, Sexton C, Siddiqi A. Virtual Reality as a novel educational tool in pre‐clinical paediatric dentistry training: Students’ perceptions. International Journal of Paediatric Dentistry. 2020;30(6):791–7. doi: 10.1111/ipd.12648.
Mladenovic R, Dakovic D, Pereira L, Matvijenko V, Mladenovic K. Effect of augmented reality simulation on administration of local anaesthesia in paediatric patients. European Journal Dental Education. 2020;24(3):507–12. doi: 10.1111/eje.12529.
Queen Mary University of London [Internet]. 2022. A nervous child patient visits the dental clinic - digital education studio. Available from: https://www.qmul.ac.uk/digital-education-studio/our-work/fmd-immersive-learning-lab/nervous-child-patient-vr/
Papadopoulos L, Pentzou AE, Louloudiadis K, Tsiatsos TK. Design and evaluation of a simulation for pediatric dentistry in virtual worlds. Journal of Medical Internet Research. 2013;15(10):e240. doi:10.2196/jmir.2651.
Huang S, Wen C, Bai X, Li S, Wang S, Wang X, et al. Exploring the application capability of ChatGPT as an instructor in skills education for dental medical students: randomized controlled trial. Journal of Medical Internet Research. 2025;27:e68538. doi:10.2196/68538.
Bryce M, Burns L, Ahmadi H, Hanks S, Nasser M, Zhou SM. Artificial intelligence in dental service provision: A rapid evidence assessment. London: General Dental Council; 2025. ISBN: 978-1-911654-18-6.
