Articles | Open Access | https://doi.org/10.55640/

ARTIFICIAL INTELLIGENCE IN DENTISTRY: IMPROVING DIAGNOSIS, TREATMENT PLANNING, AND PATIENT CARE

Komilova Muslima Odiljon kizi,Оlimova Мubinabonu Оdilbek kizi , Kokand University, Andijan Branch Faculty of Medicine

Abstract

This study explores the application of artificial intelligence (AI) in dentistry, focusing on its role in enhancing diagnostic accuracy, treatment planning, and overall patient care. The research highlights how AI technologies, including machine learning, neural networks, and imaging analysis, can support dentists in early detection of dental diseases such as caries, periodontal conditions, and oral cancers. Additionally, the study discusses the benefits of AI in predicting treatment outcomes, optimizing personalized care, and improving clinical efficiency. Ethical considerations, potential challenges, and future prospects of integrating AI into dental practice are also addressed. The findings suggest that AI has the potential to revolutionize dentistry, making it more precise, efficient, and patient-centered.

Keywords

dietary habits, dental health, tooth enamel, caries, sugar, acids, vitamins, minerals, oral microbiome, oral hygiene, healthy nutrition, prevention, oral diseases.

References

Schwendicke, F., Samek, W., & Krois, J. (2020). Artificial intelligence in dentistry: Chances and challenges. Journal of Dental Research, 99(7), 769–774. https://doi.org/10.1177/0022034520915713

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. https://doi.org/10.1016/j.jdent.2018.07.014

Devlin, H., & Le, T. (2021). Applications of artificial intelligence in oral and maxillofacial radiology. Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology, 131(6), 666–673. https://doi.org/10.1016/j.oooo.2021.02.007

Khanagar, S.B., Al-Ehaideb, A., Vishwanathaiah, S., et al. (2021). Artificial intelligence in dentistry: Current applications and future perspectives. Healthcare, 9(7), 836. https://doi.org/10.3390/healthcare9070836

Poedjiastoeti, W., & Suebnukarn, S. (2019). Detection of dental caries in bitewing radiographs using deep learning. Dentomaxillofacial Radiology, 48(1), 20180051. https://doi.org/10.1259/dmfr.20180051

Joda, T., & Gallucci, G.O. (2019). The virtual patient in dentistry: A review of current applications. Clinical Oral Investigations, 23, 1–12. https://doi.org/10.1007/s00784-019-02881-2

Schwendicke, F., Golla, T., Dreher, M., & Krois, J. (2021). Artificial intelligence for oral healthcare: A systematic review. Journal of Dentistry, 107, 103610. https://doi.org/10.1016/j.jdent.2021.103610

Chen, H., Zhang, K., Lyu, P., et al. (2020). Deep learning for dental images: A review. Computers in Biology and Medicine, 123, 103867. https://doi.org/10.1016/j.compbiomed.2020.103867

Hung, M., & Rajasekaran, S. (2022). AI-powered tools in orthodontics: Revolutionizing diagnosis and treatment planning. American Journal of Orthodontics and Dentofacial Orthopedics, 161(4), 529–537. https://doi.org/10.1016/j.ajodo.2021.11.018

Schwendicke, F., Samek, W., & Krois, J. (2021). Explainable artificial intelligence in dentistry. Journal of Dental Research, 100(5), 459–466. https://doi.org/10.1177/0022034521994125

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ARTIFICIAL INTELLIGENCE IN DENTISTRY: IMPROVING DIAGNOSIS, TREATMENT PLANNING, AND PATIENT CARE. (2025). International Journal of Medical Sciences, 5(10), 490-493. https://doi.org/10.55640/