The Evolution and Impact of Artificial Intelligence in Orthognathic Surgery: A Bibliometric Analysis.

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Abstract

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are reshaping orthognathic surgery and maxillofacial traumatology by supporting diagnostic imaging and three-dimensional virtual surgical planning (VSP). This study provides a bibliometric mapping of the field in order to characterise its thematic structure, trace its temporal evolution, describe international collaboration patterns, and situate the clinical limitations reported in the literature. A systematic search was conducted in Scopus, PubMed/MEDLINE, and IEEE Xplore from database inception to 24 August 2026, restricted to English-language records. The query combined three blocks: artificial intelligence terminology ("artificial intelligence", "machine learning", "deep learning", "neural network*", "machine intelligence"), craniomaxillofacial terms ("orthognathic", "maxillofacial", "mandibular split", "le fort", "stomatognathic"), and surgical terms ("surgery", "surgical", "planning", "osteotomy"). Records were eligible when they both implemented an AI method and applied it to the craniomaxillofacial or dental domain, including educational and patient-communication applications; records were excluded as editorials, errata, letters and short opinion pieces, conference proceedings volumes, studies without an AI method, medical head-and-neck oncology, three-dimensional facial reconstruction without craniomaxillofacial surgical application, non-clinical computer vision, or topics outside the craniomaxillofacial domain. The search retrieved 1,925 records; after removal of 539 duplicates, 1,386 unique records underwent title and abstract screening, and 854 documents met the eligibility criteria. Bibliometric networks were constructed in VOSviewer (version 1.6.21; Centre for Science and Technology Studies (CWTS), Leiden University, Netherlands). Keyword co-occurrence analysis resolved five thematic clusters, centred respectively on generative AI and language models, orthognathic surgery and cephalometry, dentomaxillary radiological imaging, DL methodology, and computer-assisted surgery with digital dentistry. Temporal overlay analysis showed that keywords relating to large language models and educational applications carry the most recent average publication years, whereas cephalometric and landmark detection terms are comparatively older. Country-level co-authorship analysis identified 35 countries meeting a five-document threshold, organised into six regional clusters, with China and the United States as the principal contributors by output and the United States as the most central connector. These bibliometric observations indicate a field in rapid thematic transition, in which methodological heterogeneity, dataset homogeneity, and limited algorithmic interpretability remain the barriers most frequently reported to clinical translation.

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