ABSTRACT

Artificial intelligence (AI) has increasingly shaped research and clinical practice in oral and maxillofacial radiology, particularly with the widespread use of digital imaging modalities. The rapid growth of AI-related publications has resulted in an expanding body of literature, making it difficult to comprehensively evaluate research trends and thematic developments in the field.
This study aimed to conduct a bibliometric analysis of scientific publications focusing on artificial intelligence applications in oral and maxillofacial radiology. A literature search was performed using the Web of Science Core Collection database, covering publications from 1994 to 2025. Bibliometric analyses were carried out using the bibliometrix package and Biblioshiny to assess publication trends, citation patterns, leading journals, authors, countries, institutions, and keyword-based conceptual structures. Artificial intelligence–supported analytical methods were applied to examine thematic structures within the literature.
The results showed a marked increase in AI-related research output after 2018, with China, the United States, and South Korea emerging as the most productive countries. Keyword co-occurrence analysis identified dominant thematic clusters related to artificial intelligence, deep learning, machine learning, and classification, highlighting the prominence of data-driven approaches in image analysis and diagnosis.
Overall, this bibliometric analysis provides a structured overview of the evolution and current landscape of artificial intelligence research in oral and maxillofacial radiology, emphasizing the growing integration of AI-driven methodologies within the field.

KEYWORDS

Artificial Intelligence, Deep Learning, Bibliometrics, Radiology.

REFERENCES

1) Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017;42: 60-88.
2) Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature medicine 2019;25(1): 44-56. 
3) Suganyadevi S, Seethalakshmi V, Balasamy K. A review on deep learning in medical image analysis. Int J Multimed Inf Retrl 2022;11(1): 19-38.
4) Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nature medicine 2019;25(1): 24-29.
5) Schwendicke FA, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. J Dent Res 2020;99(7): 769-774.
6) Dinggang Shen, Guorong Wu, Heung-Il Suk. Deep Learning in Medical Image Analysis. Annul Rev Biomed Eng 2017; 19:221-248.
7) Chen YW, Stanley K, Att W. Artificial intelligence in dentistry: current applications and future perspectives. Quintessence Int 2020;51(3): 248-57.
8) Lee JH, Kim DH, Jeong SN. Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network. Oral Dis 2020;26(1): 152-158.
9) Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. Dentomaxillofac Radiol 2020;49(1): 20190107
10) Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. How to conduct a bibliometric analysis: An overview and guidelines. J Bus Res 2021; 133:285-296.
11) Aria M, Cuccurullo C. bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of informetrics 2017;11(4): 959-975.
12) Ogunleye B, Lancho Barrantes BS, Zakariyyah KI. Topic modelling through the bibliometrics lens and its technique. Artificial Intelligence Review 2025;58(3), 74.
13) Callon M, Courtial JP, Laville F. Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemsitry. Scientometrics 1991;22(1), 155-205.
14) Thrall JH, Li X, Li Q, Cruz C, Do S, Dreyer K, et al. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol 2018;15(3): 504-508.
15) Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp 2018;2(1): 35.
16) Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. radiographics, 2017;37(2): 505-515.
17) Yilmaz S, Tasyurek M, Amuk M, Celik M, Canger EM. Developing deep learning methods for classification of teeth in dental panoramic radiography. Oral Surg Oral Med Oral Pathol Oral Radiol 2024;138(1): 118-127.
18) Tuzoff DV, Tuzova LN, Bornstein MM, Krasnov AS, Kharchenko MA, Nikolenko SI, et al. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofac Radiol 2019;48(4): 20180051.
19) Jaskari J, Sahlsten J, Järnstedt J, Mehtonen H, Karhu K, Sundqvist O, et al. Deep learning method for mandibular canal segmentation in dental cone beam computed tomography volumes. Sci Rep 2020;10(1): 5842.
20) Chen C. Science mapping: a systematic review of the literature. J Data Inf Sci 2017;2(2).
21) Cobo MJ, López‐Herrera AG, Herrera‐Viedma E, Herrera F. Science mapping software tools: Review, analysis, and cooperative study among tools. J Am Soc Inf Sci Technol 2011;62(7): 1382-1402.
22) Zupic I, Čater T. Bibliometric methods in management and organization. Organ Res Methods 2015;18(3): 429-472.
23) Langlotz CP. Will artificial intelligence replace radiologists?. Radiol Artif Intell 2019;1(3): e190058.
24) Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJ. Artificial intelligence in radiology. Nat Rev Cancer 2018;18(8): 500-510.
25) McBee MP, Awan OA, Colucci AT, Ghobadi CW, Kadom N, Kansagra AP, et al. Deep learning in radiology. Acad Radiol 2018;25(11): 1472-1480.