ArticleInternational dental journal2026
Mapping Artificial Intelligence Research in Oral and Maxillofacial Surgery: A Bibliometric Analysis.
Article in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
INTRODUCTION AND
aimsOral and maxillofacial surgery (OMFS) produces complex clinical data, while artificial intelligence (AI) applications in this field remain limited despite great potential. This study aimed to map the current status, hotspots, and emerging trends of AI in OMFS.
methodsPublications on AI and OMFS before January 10, 2025, were retrieved from the Web of Science Core Collection and Scopus. Data were analysed using CiteSpace and Carrot
resultsA total of 5267 articles were included. The co-citation network showed a research base dominated by AI-driven medical image analysis, with a shift from traditional machine learning to deep learning and transformer models. Carrot
conclusionAI research in OMFS is currently centered on medical image analysis, particularly radiomics and pathology imaging. Methodological advances have shifted toward deep learning and transformer-based approaches, with ChatGPT as a representative model. Non-imaging applications, including pathway and prognostic analyses, represent promising directions for future integration of AI into OMFS. CLINICAL RELEVANCE: AI applications, particularly imaging-driven models and transformer-based architectures, offer practical tools for diagnosis, surgical planning, and prognostic assessment in OMFS. Transfer learning enables effective adaptation of AI models to institution-specific datasets, facilitating personalised patient management. By highlighting emerging opportunities in pathway analysis and outcome prediction, this study informs clinicians of actionable AI strategies, supporting evidence-based integration into routine practice and guiding the adoption of novel computational approaches for improved patient care.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.