To quantitatively analyze the publication volume and growth patterns of AI-assisted diagnosis of oral diseases, identifying leading countries, institutions, and core scholars, while mapping collaborative networks.
Approach:
Data Acquisition: The study searched the Web of Science Core Collection for publications on AI-assisted diagnosis of oral diseases from 2005 to 2025, ultimately including 2131 studies after screening.
Data Analysis: Bibliometric analysis was performed using CiteSpace to analyze publications, countries, institutions, authors, journals, citations, co-occurrence, and keyword clustering.
Key Findings:
AI has shown significant potential in improving the speed, accuracy, and efficiency of diagnosing oral diseases.
Current research hotspots in AI for dentistry include disease diagnosis, orthodontic intervention, and maxillofacial morphological segmentation.
Interpretation:
The findings provide an overview of the status quo and development trend of AI-based oral disease diagnosis, highlighting the need for further studies in this area.
Limitations:
Research in AI-based oral diagnostics remains piecemeal and methodologically limited.
Conclusion:
The study elucidates core themes and emerging frontier fields in AI-assisted oral disease diagnosis.
