Artificial Intelligence for Radiographic Caries Detection in Dental Education: A Narrative Review

Authors

  • Samandar S. Alikhodjaev Faculty of Dentistry, Kimyo International University in Tashkent, Tashkent, Uzbekistan Author
  • Nilufar Kh. Abdukhalimova Faculty of Dentistry, Kimyo International University in Tashkent, Tashkent, Uzbekistan Author
  • Sardorkhon G. Usmonov Faculty of Dentistry, Kimyo International University in Tashkent, Tashkent, Uzbekistan Author
  • Davron Kh. Sunnatov Faculty of Dentistry, Kimyo International University in Tashkent, Tashkent, Uzbekistan Author
  • Yasmina A. Bekmirzayeva Faculty of Dentistry, Kimyo International University in Tashkent, Tashkent, Uzbekistan Author
  • Ulugbek A. Khatamov Faculty of Dentistry, Kimyo International University in Tashkent, Tashkent, Uzbekistan Author

Keywords:

Artificial intelligence, deep learning, dental caries

Abstract

Radiographic caries detection is a core competency in undergraduate dentistry, yet both novices and experienced clinicians show limited sensitivity for early lesions and considerable inter-examiner variability. Deep learning–based systems now detect caries on intraoral radiographs with high accuracy, raising the question of how they should be used in teaching.

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Published

2026-10-06

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Section

Articles

How to Cite

Artificial Intelligence for Radiographic Caries Detection in Dental Education: A Narrative Review. (2026). Global Insights in Biomedical & Multidisciplinary Research, 1(08), 1-12. https://biomedglobe.com/index.php/gibmr/article/view/89

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