Enhancing oral health diagnosis and treatment with artificial intelligence in dentistry
Keywords:
oral health, diagnosis, artificial intelligence, dentistryAbstract
This study aims to explore the potential of artificial intelligence (AI) in enhancing oral health diagnosis and treatment in the field of dentistry. With a sample size of 100 individuals from the population of Lahore, this research investigates the efficacy of AI technologies in revolutionizing oral healthcare practices. The traditional approach to oral health diagnosis and treatment heavily relies on subjective human judgment, leading to variations in diagnoses and treatment plans. By leveraging AI algorithms, this study demonstrates how machine learning and deep learning techniques can assist dentists in making accurate and efficient diagnoses, resulting in improved treatment outcomes. The research involves the development of an AI model trained on a dataset comprising dental records and associated clinical information. Through the analysis of this dataset, the AI model learns to recognize patterns, identify abnormalities, and predict potential oral health issues. By integrating this model into dental practices, dentists can make informed decisions based on data-driven insights, ultimately leading to enhanced patient care. Furthermore, this study evaluates the potential challenges and ethical considerations associated with implementing AI in dentistry.
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Beaulieu-Jones, B. K., Greene, C. S., & Tatonetti, N. P. (2018). Computational approaches for interpreting genomic sequence variation. JAMA, 320(18), 1924-1925.
Cheetha, S., Abu-Mostafac, A., Carstena, O., Dinndorf, R., Kinnebrock, S., & Ständer, M. (2019). Towards the use of artificial intelligence in oral and maxillofacial surgery: Ethical implications. Journal of Cranio-Maxillofacial Surgery, 47(11), 1781-1787.
Choi, W. T., Das, P., & Zhang, K. (2020). Deep learning-based risk prediction models for dental caries. Proceedings of the AAAI Conference on Artificial Intelligence in Healthcare, 2020(1), 103-109.
Dehbozorgi, A., Luo, L., Wang, L., Zhang, Y., Xie, L., Zhao, X., & Tang, Z. (2021). Deep learning for oral diseases: A comprehensive review. Oral Diseases, 27(3), 442-451.
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
Liaw, C. Y., Huang, Y. C., Chen, M. H., & Tsai, M. T. (2020). Application of machine learning to predict the outcome of orthodontic treatment: A systematic review. Journal of Clinical Medicine, 9(4), 1033.
Shen, S. H., Hung, C. C., Wu, Y. F., & Fu, E. (2020). Computer-aided implant dentistry: Implant position surgical guide and surgical guide system. Journal of Dental Sciences, 15(4), 427- 432.
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