Artificial Intelligence in Oral Health Care
Article Sidebar
Main Article Content
Abstract
Artificial intelligence (AI) has been continuously developed and increasingly applied in medicine and dentistry, particularly in radiographic image analysis, disease diagnosis, treatment planning, and clinical decision-support systems. This technology has the potential to improve the accuracy of oral disease diagnosis, such as dental caries, periodontal disease, and periapical lesions. It can also enhance the efficiency of dental services and reduce variability among examiners in radiographic interpretation.
This study aimed to review the body of knowledge regarding the application of artificial intelligence in dentistry and to analyze the potential, limitations, and challenges of using this technology in oral healthcare. A narrative literature review was conducted using PubMed, Scopus, and Google Scholar databases. The articles included in the analysis were limited to research studies published between 2019 and 2025, while important studies published before 2019 were used to provide background information in the introduction.
The review found that deep learning techniques, particularly Convolutional Neural Networks (CNNs), were the most commonly used methods for dental radiographic image analysis and could effectively assist in detecting dental abnormalities. However, the application of AI still has limitations, including dataset diversity, algorithm transparency, and patient data privacy. Therefore, future development should focus on improving high-quality datasets, testing systems in real clinical environments, and establishing appropriate regulatory guidelines. In addition, the clinical application of AI should carefully consider ethical and legal limitations, particularly the protection of patients’ personal data and professional liability.
Article Details
References
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015, doi: 10.1038/nature14539.
G. Litjens et al., “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60–88, 2017, doi: 10.1016/j.media.2017.07.005.
A. Esteva et al., “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115–118, 2017, doi: 10.1038/nature21056.
D. Albano et al., “Artificial intelligence for radiographic imaging detection of caries lesions: A systematic review,” BMC Oral Health, vol. 24, no. 1, Art. no. 274, 2024, doi: 10.1186/s12903-024-04046-7.
J.-H. Lee, D.-H. Kim, S.-N. Jeong, and S.-H. Choi, “Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm,” Journal of Dentistry, vol. 77, pp. 106–111, 2018, doi: 10.1016/j.jdent.2018.07.015.
J. Krois et al., “Deep learning for the radiographic detection of periodontal bone loss,” Scientific Reports, vol. 9, Art. no. 8495, 2019, doi: 10.1038/s41598-019-44839-3.
A. G. Cantu et al., “Detecting caries lesions of different radiographic extension on bitewings using deep learning,” Journal of Dentistry, vol. 100, Art. no. 103425, 2020, doi: 10.1016/j.jdent.2020.103425.
A. Aminoshariae, J. Kulild, and V. Nagendrababu, “Artificial intelligence in endodontics: Current applications and future directions,” Journal of Endodontics, vol. 47, no. 9, pp. 1352–1357, 2021, doi: 10.1016/j.joen.2021.06.003.
T. Ekert et al., “Deep learning for the radiographic detection of apical lesions,” Journal of Endodontics, vol. 45, no. 7, pp. 917–922, 2019, doi: 10.1016/j.joen.2019.03.016.
K. Orhan, I. S. Bayrakdar, M. Ezhov, A. Kravtsov, and T. Özyürek, “Evaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans,” International Endodontic Journal, vol. 53, no. 5, pp. 680–689, 2020, doi: 10.1111/iej.13265.
S. Mertens, J. Krois, A. Garcia Cantu, L. T. Arsiwala, and F. Schwendicke, “Artificial intelligence for caries detection: Randomized trial,” Journal of Dentistry, vol. 115, Art. no. 103849, 2021, doi: 10.1016/j.jdent.2021.103849.
H. Mohammad-Rahimi et al., “Deep learning for caries detection: A systematic review,” Journal of Dentistry, vol. 122, Art. no. 104115, 2022, doi: 10.1016/j.jdent.2022.104115.
G. Litjens et al., “A survey on deep learning in medical image analysis,” Medical Image Analysis, vol. 42, pp. 60–88, 2017, doi: 10.1016/j.media.2017.07.005.
E. J. Topol, “High-performance medicine: The convergence of human and artificial intelligence,” Nature Medicine, vol. 25, no. 1, pp. 44–56, 2019, doi: 10.1038/s41591-018-0300-7.
S. B. Khanagar et al., “Developments, application, and performance of artificial intelligence in dentistry: A systematic review,” Journal of Dental Sciences, vol. 16, no. 1, pp. 508–522, 2021, doi: 10.1016/j.jds.2020.06.019.
W. J. Park, J. B. Choi, and J. H. Kim, “Detection and diagnosis of dental caries using deep learning algorithm,” Journal of Dental Sciences, vol. 13, no. 4, pp. 382–388, 2018, doi: 10.1016/j.jds.2018.08.003.
N. Turosz, K. Chęcińska, M. Chęciński, A. Brzozowska, Z. Nowak, and M. Sikora, “Applications of artificial intelligence in the analysis of dental panoramic radiographs: An overview of systematic reviews,” Dentomaxillofacial Radiology, vol. 52, no. 7, Art. no. 20230284, 2023, doi: 10.1259/dmfr.20230284.
V. Szabó et al., “Validation of artificial intelligence application for dental caries diagnosis on intraoral bitewing and periapical radiographs,” Journal of Dentistry, vol. 147, Art. no. 105105, 2024, doi: 10.1016/j.jdent.2024.105105.
E. Sivari, E. Senirkentli, G. B. Bostanci, M. S. Guzel, K. Acici, and T. Asuroglu, “Deep learning in diagnosis of dental anomalies and diseases: A systematic review,” Diagnostics, vol. 13, no. 15, Art. no. 2512, 2023, doi: 10.3390/diagnostics13152512.
P. Rajpurkar, E. Chen, O. Banerjee, and E. J. Topol, “AI in health and medicine,” Nature Medicine, vol. 28, no. 1, pp. 31–38, 2022, doi: 10.1038/s41591-021-01614-0.
M. Moor et al., “Foundation models for generalist medical artificial intelligence,” Nature, vol. 616, no. 7956, pp. 259–265, 2023, doi: 10.1038/s41586-023-05881-4.
Office of the Council of State, “Personal Data Protection Act B.E. 2562 (2019),” Government Gazette, vol. 136, no. 69 A, pp. 1–21, 2019. (in Thai)
