Objective: This study aims to evaluate the literature on the use of deep learning models in the diagnosis of dental lesions through bibliometric mapping analysis.

Material and Method: Studies indexed in the Web of Science Core Collection database using relevant keywords were examined within the scope of bibliometric analysis. Publication year, citation numbers, countries, journals, authors, keywords, and collaboration networks were analyzed using VOSviewer software.

Results: The analysis results showed that the number of publications on deep learning applications in the diagnosis of dental lesions increased significantly, especially after 2018. The keywords “deep learning”, “artificial intelligence”, and “machine learning” were found to have a central position in the literature. South Korea, China, and the United States were among the countries with the highest citations, while clinical and radiology-focused journals were found to be prominent in the field.

Conclusion: This bibliometric analysis reveals research trends, prominent areas of study, and existing knowledge gaps regarding deep learning applications in the diagnosis of dental lesions. It is expected that the findings will guide future studies and contribute to the development of artificial intelligence-based diagnostic approaches in dental radiology.

Dental lesions Deep learning Artificial intelligence Bibliometric analysis Dental radiology