JDSE
Journal of Dental Sciences and Education

The Journal of Dental Sciences and Education deals with General Dentistry, Pediatric Dentistry, Restorative Dentistry, Orthodontics, Oral diagnosis and DentomaxilloFacial Radiology, Endodontics, Prosthetic Dentistry, Periodontology, Oral and Maxillofacial Surgery, Oral Implantology, Dental Education and other dentistry fields and accepts articles on these topics. Journal of Dental Science and Education publishes original research articles, review articles, case reports, editorial commentaries, letters to the editor, educational articles, and conference/meeting announcements.

eISSN: 2980-1389
Creative Commons CC BY-NC-ND 4.0 International License.
EndNote Style

Online Manuscript System

Index
Original Article
A bibliometric analysis of the literature on the use of artificial intelligence in pediatric dentistry
Abstract
Aims: This study aims to conduct a bibliometric analysis of the scientific literature on the use of artificial intelligence (AI) in pediatric dentistry to evaluate publication trends, citation impact, key contributors, and research themes.
Methods: A comprehensive literature search was conducted in the Web of Science (WoS) database up to February 8, 2025. The search included AI-related terms combined with pediatric dentistry keywords. A total of 78 relevant articles were identified and analyzed. VOSviewer software was used for bibliometric mapping, including co-authorship, co-citation, and keyword analysis.
Results: The number of publications on AI in pediatric dentistry has increased significantly since 2020, peaking in 2024, followed by a decline in 2025. The analysis identified key research topics, including diagnostic imaging, early childhood caries detection, dental age estimation, and orthodontic assessments. Despite the growth in research output, AI applications in pediatric dentistry remain significantly underdeveloped compared to other dental fields. Citation impact was relatively low, with the most referenced article receiving 83 citations.
Conclusion: AI is gaining attention in pediatric dentistry; however, its adoption is still in the early stages. Further research is needed to validate AI models, enhance clinical integration, and expand interdisciplinary collaboration. Addressing data limitations and improving real-world applicability will be crucial for AI’s long-term impact on pediatric dental care.

Keywords : Artificial intelligence, deep learning, bibliometrics, pediatric dentistry

Download Article


1. Rajaraman V. John McCarthy-father of artificial intelligence. Resonance. 2014;19(3):198-207. doi:10.1007/s12045-014-0027-9
2. Li JO, Liu H, Ting DSJ, et al. Digital technology, tele-medicine and artificial intelligence in ophthalmology: a global perspective. Prog Retin Eye Res. 2021;82:100900. doi:10.1016/j.preteyeres.2020.100900
3. Mohammad-Rahimi H, Sohrabniya F, Ourang SA, et al. Artificial intelligence in endodontics: data preparation, clinical applications, ethical considerations, limitations, and future directions. Int Endod J. 2024;57(11):1566-1595. doi:10.1111/iej.14128
4. Vishwanathaiah S, Fageeh HN, Khanagar SB, Maganur PC. Artificial intelligence its uses and application in pediatric dentistry: a review. Biomedicines. 2023;11(3):788. doi:10.3390/biomedicines11030788
5. Agrawal P, Nikhade P. Artificial intelligence in dentistry: past, present, and future. Cureus. 2022;14(7):e27405. doi:10.7759/cureus.27405
6. Nguyen TT, Larrivée N, Lee A, Bilaniuk O, Durand R. Use of artificial intelligence in dentistry: Current clinical trends and research advances. J Can Dent Assoc. 2021;87(l7):1488-2159.
7. Dave VS, Dutta K. Neural network-based models for software effort estimation: a review. Artif Intell Rev. 2014;42(2):295-307. doi:10.1007/s10462-012-9339-x
8. Bichu YM, Hansa I, Bichu AY, Premjani P, Flores-Mir C, Vaid NR. Applications of artificial intelligence and machine learning in orthodontics: a scoping review. Prog Orthod. 2021;22(1):18. doi:10.1186/s40510-021-00361-9
9. Bouletreau P, Makaremi M, Ibrahim B, Louvrier A, Sigaux N. Artificial intelligence: applications in orthognathic surgery. J Stomatol Oral Maxillofac Surg. 2019;120(4):347-354. doi:10.1016/j.jormas.2019.06.001
10. Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework, and future research agenda. J Ambient Intell Humaniz Comput. 2023;14(7): 8459-8486. doi:10.1007/s12652-021-03612-z
11. Thompson DF, Walker CK. A descriptive and historical review of bibliometrics with applications to medical sciences. Pharmacother J Hum Pharmacol Drug Ther. 2015;35(6):551-559. doi:10.1002/phar.1586
12. van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010;84(2):523-538. doi:10.1007/s11192-009-0146-3
13. You W, Hao A, Li S, Wang Y, Xia B. Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments. BMC Oral Health. 2020;20(1):141. doi:10.1186/s12903-020-01114-6
14. Ezhov M, Gusarev M, Golitsyna M, et al. Clinically applicable artificial intelligence system for dental diagnosis with CBCT. Sci Rep. 2021;11(1): 15006. doi:10.1038/s41598-021-94093-9
15. Kılıc MC, Bayrakdar IS, Çelik Ö, et al. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. Dentomaxillofac Radiol. 2021;50(6):20200172. doi:10.1259/dmfr.20200172
16. Zaborowicz M, Zaborowicz K, Biedziak B, Garbowski T. Deep learning neural modelling as a precise method in the assessment of the chronological age of children and adolescents using tooth and bone parameters. Sensors (Basel). 2022;22(2):637. doi:10.3390/s22020637
17. Liu J, Liu Y, Li S, Ying S, Zheng L, Zhao Z. Artificial intelligence-aided detection of ectopic eruption of maxillary first molars based on panoramic radiographs. J Dent. 2022;125:104239. doi:10.1016/j.jdent. 2022.104239
18. Caliskan S, Tuloglu N, Celik O, Ozdemir C, Kizilaslan S, Bayrak S. A pilot study of a deep learning approach to submerged primary tooth classification and detection. Int J Comput Dent. 2021;24(1):1-9. doi:10. 3290/j.ijcd.b994539
19. Kaya E, Gunec HG, Aydin KC, Urkmez ES, Duranay R, Ates HF. A deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs. Imaging Sci Dent. 2022;52(3):275-281. doi:10. 5624/isd.20220050
20. Rajinikanth SB, Rajkumar DSR, Rajinikanth A, Anandhapandian PA. An overview of artificial intelligence based automated diagnosis in paediatric dentistry. Front Oral Health. 2024;5:1482334. doi:10.3389/froh. 2024.1482334
21. Knox J. Artificial intelligence and education in China. Learn Media Technol. 2020;45(3):298-311. doi:10.1080/17439884.2020.1754236
Article available in :
Volume 3, Issue 1, 2025
Page : 1-7
https://doi.org/10.51271/JDSE-0046
Article Information
Received : 01 Şub 2025
Accepted : 24 Şub 2025
Published : 28 Şub 2025
Corresponding Author :

İbrahim Tevfik Gülşen: Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Alanya Alaaddin Keykubat University, Antalya, Turkiye

[email protected]
Citation : Gülşen E, Gülşen İT, Mahyaddinova T. A bibliometric analysis of the literature on the use of artificial intelligence in pediatric dentistry. J Dent Sci Educ. 2025;3(1):1-7.

Read: 105
Cited: 0
Download : 44
_Footer