Аннотация
The integration of Artificial Intelligence (AI) into dental diagnostics has emerged as a transformative development in dental education. This study investigates the impact of AI assistance on the clinical and radiographic diagnostic decisions of dental students during dental caries detection. While traditional training relies heavily on visual-tactile metrics and conventional radiographic interpretation, AI systems utilizing Convolutional Neural Networks (CNNs) offer a standardized, data-driven second opinion. Current literature suggests that AI assistance substantially improves students' diagnostic accuracy and sensitivity, particularly in identifying early enamel and proximal carious lesions. Furthermore, AI tools reduce diagnostic time and enhance student confidence. However, concerns regarding automation bias, potential false positives, and a deficiency in structured curricula persist. This paper outlines the mechanisms, educational benefits, limitations, and future outlook of implementing AI in dental diagnostics training.
Библиографические ссылки
Sapci, A. H., & Sapci, H. A. (2024). Dental student application of artificial intelligence technology in detecting proximal caries lesions. Journal of Dental Education, 88(4), 450-458.
Valenzuela, M., et al. (2026). Performance evaluation of AI-based caries detection technology and its educational training module: a dual-phase investigation. Frontiers in Dental Medicine, 7, 1741855.
Al-Maweri, S. A., et al. (2025). Assessing the Role of Artificial Intelligence in Caries Detection and Clinical Decision-Making: A Scoping Review. Caries Research, 59(2), 110-123.
Kim, J., et al. (2026). Effect of AI-assisted caries annotation on dental students’ diagnostic performance and confidence. BMC Medical Education, 26, 9950.
Schwendicke, F., Rossi, J. G., & Krois, J. (2024). Artificial intelligence for caries detection: A comprehensive review of diagnostic accuracy and educational implications. Journal of Dentistry, 141, 104812.
Muhammad, A., et al. (2025). Cognitive impact and automation bias in dental students utilizing AI diagnostic assistants for radiographic caries assessment. European Journal of Dental Education, 29(3), 312–320.
Park, S. H., & Kim, H. Y. (2025). Deep learning-based convolutional neural networks in dental education: Enhancing proximal caries detection skills among undergraduate students. International Journal of Computerized Dentistry, 28(1), 45–54.
Garcia, M. R., et al. (2026). Impact of AI-assisted diagnostic tools on the clinical decision-making flow and visual focus of novice dental clinicians. Clinical Oral Investigations, 30, 11425.
Thomas, A. L., & Roberts, P. J. (2026). Integrating artificial intelligence into the dental curriculum: A multi-center study on student perception and diagnostic proficiency. Journal of Dental Education, 90(2), 204–213.