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Mapping Artificial Intelligence Research in Oral and Maxillofacial Surgery: A Bibliometric Analysis
Yingzhao Huang1, Yuhong Wang1, Chen Hou1
1Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, Guangdong, China.
International Dental Journal
|March 3, 2026
Summary
Artificial intelligence (AI) in oral and maxillofacial surgery (OMFS) is advancing, focusing on medical image analysis with deep learning and transformer models. Future directions include pathway analysis and prognostic modeling for improved patient care.
Area of Science:
- Oral and Maxillofacial Surgery (OMFS)
- Artificial Intelligence (AI)
- Medical Imaging Analysis
Background:
- OMFS generates complex data, but AI applications are underdeveloped despite significant potential.
- Current research focuses on mapping the status, hotspots, and trends of AI in OMFS.
Purpose of the Study:
- To analyze the current landscape of AI in OMFS.
- To identify research hotspots and emerging trends in AI for OMFS.
Main Methods:
- Bibliometric analysis of 5267 articles from Web of Science and Scopus (up to January 10, 2025).
- Utilized CiteSpace and Carrot^2 for co-citation, structural variation, and term co-occurrence network analysis.
- Focused on a decade-long trend analysis.
Main Results:
- AI in OMFS is dominated by medical image analysis, shifting from machine learning to deep learning and transformer models.
- Radiomics and pathology imaging are key areas, with emerging applications in prognostic modeling for head and neck cancer.
- Term co-occurrence highlights applications in radiomics, head and neck cancer, pathway analysis, and orthognathic surgery.
Conclusions:
- AI research in OMFS centers on imaging (radiomics, pathology), with a methodological shift towards deep learning and transformers.
- Non-imaging applications like pathway and prognostic analyses are promising future directions.
- AI offers practical tools for diagnosis, surgical planning, and prognostic assessment, supporting personalized patient management and evidence-based practice.
