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VOLUME 23 , ISSUE 8 ( August, 2022 ) > List of Articles

ORIGINAL RESEARCH

Characteristics, Impact, and Visibility of Scientific Publications on Artificial Intelligence in Dentistry: A Scientometric Analysis

Ricardo Velasquez, John Barja-Ore, Emma Salazar-Salvatierra, Margot GutiérrezIlave, Cesar Mauricio-Vilchez, Roman Mendoza, Frank Mayta-Tovalino

Keywords : Artificial intelligence, Bibliometric analysis, Deep learning, Dentistry, Machine learning

Citation Information : Velasquez R, Barja-Ore J, Salazar-Salvatierra E, GutiérrezIlave M, Mauricio-Vilchez C, Mendoza R, Mayta-Tovalino F. Characteristics, Impact, and Visibility of Scientific Publications on Artificial Intelligence in Dentistry: A Scientometric Analysis. J Contemp Dent Pract 2022; 23 (8):761-767.

DOI: 10.5005/jp-journals-10024-3386

License: CC BY-NC 4.0

Published Online: 29-11-2022

Copyright Statement:  Copyright © 2022; The Author(s).


Abstract

Aim: To analyze the bibliometric characteristics, impact, and visibility of scientific publications on artificial intelligence (AI) in dentistry in Scopus. Materials and methods: Descriptive and cross-sectional bibliometric study, based on the systematic search of information in Scopus between 2017 and July 10, 2022. The search strategy was elaborated with Medical Subject Headings (MeSH) and Boolean operators. The analysis of bibliometric indicators was performed with Elsevier’s SciVal program. Results: From 2017 to 2022, the number of publications in indexed scientific journals increased, especially in the Q1 (56.1%) and Q2 (30.6%) quartile. Among the journals with the highest production, the majority was from the United States and the United Kingdom, and the Journal of Dental Research has the highest impact (14.9 citations per publication) and the most publications (31). In addition, the Charité – Universitätsmedizin Berlin (FWCI: 8.24) and Krois Joachim (FWCI: 10.09) from Germany were the institution and author with the highest expected performance relative to the world average, respectively. The United States is the country with the highest number of published papers. Clinical significance: There is an increasing tendency to increase the scientific production on artificial intelligence in the field of dentistry, with a preference for publication in prestigious scientific journals of high impact. Most of the productive authors and institutions were from Japan. There is a need to promote and consolidate strategies to develop collaborative research both nationally and internationally.


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  1. Badillo S, Banfai B, Birzele F, et al. An introduction to machine learning. Clin Pharmacol Ther 2020;107(4):871–885. DOI: 10.1002/cpt.1796.
  2. Olczak J, Pavlopoulos J, Prijs J, et al. Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: An introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal. Acta Orthop 2021;92(5):513–525. DOI: 10.1080/17453674.2021.1918389.
  3. Nichols JA, Herbert Chan HW, Baker MAB. Machine learning: Applications of artificial intelligence to imaging and diagnosis. Biophys Rev 2019;11(1):111–118. DOI: 10.1007/s12551-018-0449-9.
  4. Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. Artificial Intelligence Healthcare 2020:25–60. DOI: 10.1016/B978-0-12-818438-7.00002-2.
  5. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J 2019;6(2):94–98. DOI: 10.7861/futurehosp.6-2-94.
  6. Park CW, Seo SW, Kang N, et al. Artificial intelligence in health care: Current applications and issues. J Korean Med Sci 2020;35(42):e379. DOI: 10.3346/jkms.2020.35.e379.
  7. Nguyen TT, Larrivée N, Lee A, et al. Use of artificial intelligence in dentistry: Current clinical trends and research advances. J Can Dent Assoc 2021;87:l7. PMID: 34343070.
  8. Ossowska A, Kusiak A, Świetlik D. Artificial intelligence in dentistry-narrative review. Int J Environ Res Public Health 2022;19(6):3449. DOI: 10.3390/ijerph19063449.
  9. Mörch CM, Atsu S, Cai W, et al. Artificial intelligence and ethics in dentistry: A scoping review. J Dent Res 2021;100(13):1452–1460. DOI: 10.1177/00220345211013808.
  10. Shan T, Tay FR, Gu L. Application of artificial intelligence in dentistry. J Dent Res 2021;100(3):232–244. DOI: 10.1177/0022034520969115.
  11. Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: Chances and challenges. J Dent Res 2020;99(7):769–774. DOI: 10.1177/0022034520915714.
  12. Tandon D, Rajawat J. Present and future of artificial intelligence in dentistry. J Oral Biol Craniofac Res 2020;10(4):391–396. DOI: 10.1016/j.jobcr.2020.07.015.
  13. Nyström ME, Karltun J, Keller C, et al. Collaborative and partnership research for improvement of health and social services: Researcher’s experiences from 20 projects. Health Res Policy Syst 2018;16(1):46. DOI: 10.1186/s12961-018-0322-0.
  14. Rycroft-Malone J, Burton CR, Wilkinson J, et al. Collective action for implementation: A realist evaluation of organisational collaboration in healthcare. Implement Sci 2016;11:17. DOI: 10.1186/s13012-016-0380-z.
  15. Jirge PR. Preparing and publishing a scientific manuscript. J Hum Reprod Sci 2017;10(1):3–9. DOI: 10.4103/jhrs.JHRS_36_17.
  16. Khanagar SB, Al-Ehaideb A, Maganur PC, et al. Developments, application, and performance of artificial intelligence in dentistry – A systematic review. J Dent Sci 2021;16(1):508–522. DOI: 10.1016/j.jds.2020.06.019.
  17. Chen YW, Stanley K, Att W. Artificial intelligence in dentistry: Current applications and future perspectives. Quintessence Int 2020;51(3):248–257. DOI: 10.3290/j.qi.a43952.
  18. Ahmed N, Abbasi MS, Zuberi F, et al. Artificial intelligence techniques: Analysis, application, and outcome in dentistry: A systematic review. Biomed Res Int 2021;2021:9751564. DOI: 10.1155/2021/9751564.
  19. Belter CW. Bibliometric indicators: Opportunities and limits. J Med Libr Assoc 2015;103(4):219–221. DOI: 10.3163/1536-5050.103.4.014.
  20. Nishiyama M, Ishibashi K, Ariji Y, et al. Performance of deep learning models constructed using panoramic radiographs from two hospitals to diagnose fractures of the mandibular condyle. Dentomaxillofac Radiol 2021;50(7):20200611. DOI: 10.1259/dmfr.20200611.
  21. Schwendicke F, Mertens S, Cantu AG, et al. Cost-effectiveness of AI for caries detection: randomized trial. J Dent 2022;119:104080. DOI: 10.1016/j.jdent.2022.104080.
  22. Krois J, Garcia Cantu A, Chaurasia A, et al. Generalizability of deep learning models for dental image analysis. Sci Rep 2021;11(1):6102. DOI: 10.1038/s41598-021-85454-5.
  23. Kosan E, Krois J, Wingenfeld K, et al. Patients’ perspectives on artificial intelligence in dentistry: A controlled study. J Clin Med 2022;11(8):2143. DOI: 10.3390/jcm11082143.
  24. Bernauer SA, Zitzmann NU, Joda T. The use and performance of artificial intelligence in prosthodontics: A systematic review. Sensors (Basel) 2021;21(19):6628. DOI: 10.3390/s21196628.
  25. Mayta-Tovalino F, Pacheco-Mendoza J, Diaz-Soriano A, et al. Bibliometric study of the National Scientific Production of All Peruvian Schools of Dentistry in Scopus. Int J Dent 2021;2021:5510209. DOI: 10.1155/2021/5510209.
  26. Poma-Castillo L, Espinoza-Poma M, Mauricio F, et al. Antifungal activity of ethanol-extracted Bixa orellana (L) (Achiote) on Candida albicans, at six different concentrations. J Contemp Dent Pract 2019;20(10):1159–1163. DOI: 10.5005/jp-journals-10024-2672.
  27. Arce J, Palacios A, Alvítez-Temoche D, et al. Tensile strength of novel nonabsorbable PTFE (Teflon®) versus other suture materials: An in vitro study. Int J Dent 2019;2019:7419708. DOI: 10.1155/2019/7419708 .
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