PDF (अंग्रेजी)
ZENODO (अंग्रेजी)
प्रकाशित
2026-03-03
खंड
Articles
##submission.howToCite##
##submission.howToCite##
МЕТОДОЛОГИЧЕСКИЕ ОСНОВЫ ПРИМЕНЕНИЯ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В СТОМАТОЛОГИИ
Ашурова С.А
Eurasian Multidisciplinary University
Хабилов Н.Л
Ташкентский Государственный Медицинский Университет
##semicolon## artificial intelligence, dentistry, machine learning, clinical validity, methodological maturity, algorithmic bias.
सार
This article presents a theoretical and methodological analysis of artificial intelligence (AI) applications in dentistry. The distinction between algorithmic performance and clinical validity is substantiated as a fundamental prerequisite for responsible clinical integration of AI systems. The use of machine learning and deep learning methods in dental diagnostics and treatment planning is examined, along with limitations related to model generalizability, data quality, and algorithmic bias.
A framework of methodological maturity levels is proposed, reflecting the transition from algorithmic testing to clinical validation and systemic integration. A three-component model (“physician – algorithm – digital infrastructure”) is developed to describe the hybrid structure of AI-assisted clinical decision-making. It is demonstrated that sustainable implementation of AI depends not only on technical performance but also on clinical validation, model interpretability, and the maturity of digital healthcare infrastructure.
##submission.citations##
1. McCarthy J, Minsky ML, Rochester N, Shannon CE. A proposal for the Dartmouth summer research project on artificial intelligence. 1955.
2. Turing AM. Computing machinery and intelligence. Mind. 1950;59:433–460. doi:10.1093/mind/LIX.236.433
3. Friedman J, Hastie T, Tibshirani R. The elements of statistical learning. New York: Springer; 2001.
4. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–444. doi:10.1038/nature14539
5. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. 2nd ed. New York: Springer; 2009.
6. Zhu X, Goldberg AB. Introduction to semi-supervised learning. Morgan & Claypool; 2009.
7. Hornik K. Approximation capabilities of multilayer feedforward networks. Neural Netw. 1991;4:251–257. doi:10.1016/0893-6080(91)90009-T
8. Schmidhuber J. Deep learning in neural networks: An overview. Neural Netw. 2015;61:85–117. doi:10.1016/j.neunet.2014.09.003
9. Kalra MK, et al. Machine learning in radiology. Radiology. 2020;295:517–529. doi:10.1148/radiol.2020191114
10. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:S36–S40. doi:10.1016/j.metabol.2017.01.011
11. Naylor CD. On the prospects for a (deep) learning health care system. JAMA. 2018;320:1099–1100. doi:10.1001/jama.2018.11129
12. Topol E. Deep medicine: how artificial intelligence can make healthcare human again. New York: Basic Books; 2019.
13. Khanagar SB, Al-Ehaideb A, Maganur PC, et al. Developments, application, and performance of artificial intelligence in dentistry – A systematic review. J Dent Sci. 2021;16:508–522. doi:10.1016/j.jds.2020.06.019
14. Norgeot B, Quer G, Beaulieu-Jones BK, et al. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nat Med. 2020;26(9):1320–1324. doi:10.1038/s41591-020-1041-y
15. Hagras H. Toward human-understandable, explainable artificial intelligence. Computer. 2018;51(9):28–36. doi:10.1109/MC.2018.3620965
16. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60:84–90.
17. Schölkopf B, Smola AJ. Learning with kernels: support vector machines, regularization, optimization, and beyond. Cambridge (MA): MIT Press; 2001.
18. Breiman L. Random forests. Mach Learn. 2001;45:5–32. Chen T, Guestrin C. XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016. p. 785–794. doi:10.1145/2939672.2939785
19. Ge R, Chen X, Chen Y. Few-shot learning for medical image analysis. Med Image Anal. 2022;75:102276. doi:10.1016/j.media.2021.102276
20. Zimmermann HJ. Fuzzy set theory—and its applications. 4th ed. New York: Springer; 2011.
21. Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. BMJ. 2020;370:m3164. doi:10.1136/bmj.m3164
22. Rivera SC, Liu X, Chan A-W, Denniston AK, Calvert MJ. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. BMJ. 2020;370:m3210. doi:10.1136/bmj.m3210
23. Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:e078378. doi:10.1136/bmj-2023-078378
24. Mongan J, Moy L, Kahn CE Jr. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell. 2020;2:e200029. doi:10.1148/ryai.2020200029
25. World Health Organization. Ethics and governance of artificial intelligence for health. Geneva: WHO; 2021.
26. World Health Organization. Regulatory considerations on artificial intelligence for health. Geneva: WHO; 2023.
27. Vest JR, Gamm LD. Health information exchange. J Am Med Inform Assoc. 2010;17:288–294. doi:10.1136/jamia.2010.003673
28. Rieke N, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3:119. doi:10.1038/s41746-020-00323-1
29. Israni ST, Verghese A. Humanizing artificial intelligence. JAMA. 2019;321(1):29–30. doi:10.1001/jama.2018.19398
