A narrative review of artificial intelligence in dental imaging: from dataset design to clinical translation.
Authors
Affiliations (6)
Affiliations (6)
- Department of Oral and Maxillofacial surgery, Korea University Anam Hospital, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.
- Interdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National University, Seoul, Republic of Korea.
- Department of Preventive & Public Health Dentistry, School of Dentistry, Seoul National University, Seoul, Republic of Korea.
- Interdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National University, Seoul, Republic of Korea. [email protected].
- Department of Oral and Maxillofacial Radiology, Dental Research Institute, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. [email protected].
- Department of Oral and Maxillofacial surgery, Korea University Anam Hospital, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea. [email protected].
Abstract
To review the development pipeline of artificial intelligence (AI) in dental imaging, focusing on dataset design, annotation quality, model development, validation, and clinical translation. Published literature on AI applications in dental imaging, including machine learning, deep learning, and foundation-model-based approaches. Peer-reviewed studies addressing imaging modalities, dataset construction, annotation protocols, model architectures, performance evaluation, and clinical translation. Studies involving panoramic radiography, intraoral radiography, cone-beam computed tomography (CBCT), and intraoral scanning were reviewed, with emphasis on dataset quality, annotation methodology, model evaluation, interpretability, and clinical implementation. AI has demonstrated considerable potential for automated classification, detection, segmentation, and decision support in dental imaging. However, challenges including dataset heterogeneity, annotation inconsistency, domain shift, information leakage, interpretability, and regulatory requirements continue to limit clinical adoption. Emerging approaches such as multimodal learning and foundation models may improve generalizability and clinical applicability. This review highlights key methodological and translational considerations beyond algorithm performance, providing guidance for developing reliable and clinically meaningful AI systems in dental imaging.