SavedAdd your journal

Journals / Anatolian Current Medical Journal / 2026 / Volume: 8 Issue: 4

Evaluation of dental students’ perceptions of Artificial Intelligence use in periodontology and implantology

Kübra Karaçam(Afyonkarahisar Sağlık Bilimleri Üniversitesi)0000-0001-5981-5253
Ayşe Toraman(SAĞLIK BİLİMLERİ ÜNİVERSİTESİ, HAMİDİYE DİŞ HEKİMLİĞİ FAKÜLTESİ, HAMİDİYE DİŞ HEKİMLİĞİ PR.)0000-0001-7988-0765
Pages
—
Publication Date
2026-07-16
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It was found that 43.4% of students were aware of the topics discussed in periodontology regarding artificial intelligence.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Aims: Artificial Intelligence (AI) has recently been attracting increasing attention in dentistry.
📊
Key Findings & Data:Methods: A 26-question online survey was distributed to dental students, covering their sociodemographic characteristics, awareness of AI in periodontology, opinions on the potential applications of AI in periodontology and implantology, and their assessments of the future of artificial intelligence.
💡
Conclusion & Impact:It was found that 43.4% of students were aware of the topics discussed in periodontology regarding artificial intelligence.
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:Periodontal diseases · dental implantation · artificial intelligence

Abstract

Aims: Artificial Intelligence (AI) has recently been attracting increasing attention in dentistry. It has the potential to support diagnosis, treatment planning, and clinical decision-making processes. This study aims to evaluate the awareness levels of dental students regarding AI technologies in the fields of periodontology and implantology. Methods: A 26-question online survey was distributed to dental students, covering their sociodemographic characteristics, awareness of AI in periodontology, opinions on the potential applications of AI in periodontology and implantology, and their assessments of the future of artificial intelligence. Statistical analyses were performed using SPSS software (Version 27.0, IBM Corp., Chicago, IL, USA), using Kruskal-Wallis and Chi-square tests where appropriate. Results: A total of 943 participants completed the questionnaire. It was found that 43.4% of students were aware of the topics discussed in periodontology regarding artificial intelligence. The awareness rate of these topics increased with the number of years of study. Based on the total score, it was determined that 4th and 5th year students had a significantly higher level of awareness regarding AI in periodontology and implantology compared to 1st and 2nd year students (p<0.05). Conclusion: Dental students in different years of study showed varying levels of awareness and perceptions regarding AI applications in periodontology, and their willingness to learn about these technologies was also positive. These results indicate the need to integrate AI technologies into the dental education curriculum.

🔖

Cite This Article

Karaçam, K., Toraman, A. (2026). Evaluation of dental students’ perceptions of Artificial Intelligence use in periodontology and implantology. Anatolian Current Medical Journal, Volume: 8 Issue: 4. https://doi.org/10.38053/acmj.1912924

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (30)

  1. Pinto Dos Santos D, Giese D, Brodehl S, et al. Medical students' attitude towards artificial intelligence: a multicentre survey. Eur Radiol. 2019; 29(4):1640-1646. doi:10.1007/s00330-018-5601-1
  2. Mascarenhas M, Afonso J, Andrade P, Cardoso H, Macedo G. Artificial intelligence and capsule endoscopy: unravelling the future. Ann Gastroenterol. 2021;34(3):300-309. doi:10.20524/aog.2021.0606
  3. Ooi SKG, Makmur A, Soon AYQ, et al. Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey. Singapore Med J. 2021;62(3):126-134. doi:10.11622/smedj.2019141
  4. Lee JH, Kim DH, Jeong SN, Choi SH. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci. 2018;48(2):114-123. doi:10.5051/jpis.2018.48.2.114
  5. Frydenlund A, Eramian M, Daley T. Automated classification of four types of developmental odontogenic cysts. Comput Med Imaging Graph. 2014;38(3):151-162. doi:10.1016/j.compmedimag.2013.12.002
  6. 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
  7. Yuzbasioglu E. Attitudes and perceptions of dental students towards artificial intelligence. J Dent Educ. 2021;85(1):60-68. doi:10.1002/jdd.12385
  8. Pauwels R, Del Rey YC. Attitude of Brazilian dentists and dental students regarding the future role of artificial intelligence in oral radiology: a multicenter survey. Dentomaxillofac Radiol. 2021;50(5):20200461. doi:10.1259/dmfr.20200461
  9. Karan-Romero M, Salazar-Gamarra RE, Leon-Rios XA. Evaluation of attitudes and perceptions in students about the use of artificial intelligence in dentistry. Dent J (Basel). 2023;11(5):125. doi:10.3390/dj11050125
  10. Taşsöker M, Akyüz M. The perspective of dental students on the use of artificial intelligence in oral radiology: a survey study. Turkiye Klinikleri J Dental Sci. 2022;28:778-783. doi:10.5336/dentalsci.2022-88041
  11. Thurzo A, Strunga M, Urban R, Surovková J, Afrashtehfar KI. Impact of artificial intelligence on dental education: a review and guide for curriculum update. Educ Sci. 2023;13(2):150. doi:10.3390/educsci13020150
  12. Chawla RL, Gadge NP, Ronad S, Waghmare A, Patil A, Deshmukh G. Knowledge, attitude and perception regarding artificial intelligence in periodontology: a questionnaire study. Cureus. 2023;15(11):e48309. doi:10.7759/cureus.48309
  13. Pullishery F, Alhejoury HA, Ahmed TH, et al. Perception of dental students on gender preferences in career choice, plans, and expectations in dentistry in the Kingdom of Saudi Arabia. J Pharm Bioallied Sci. 2021; 13(Suppl 1):S821-S825. doi:10.4103/jpbs.JPBS_803_20
  14. Hegde S, Nanayakkara S, Jordan A, et al. Attitudes and perceptions of Australian dentists and dental students towards applications of artificial intelligence in dentistry: a survey. Eur J Dent Educ. 2025;29(1):9-18. doi:10.1111/eje.13042
  15. Kalaimani G, B S, Chockalingam RM, Karthick P. Evaluation of knowledge, attitude, and practice (KAP) of artificial intelligence among dentists and dental students: a cross-sectional online survey. Cureus. 2023;15(9):e44656. doi:10.7759/cureus.44656
  16. Papantonopoulos G, Takahashi K, Bountis T, Loos BG. Artificial neural networks for the diagnosis of aggressive periodontitis trained by immunologic parameters. PLoS One. 2014;9(3):e89757. doi:10.1371/journal.pone.0089757
  17. Jafer M, Ibraheem W, Dawood T, et al. The application and performance of artificial intelligence (AI) models in the diagnosis, classification, and prediction of periodontal diseases: a systematic review. Diagnostics (Basel). 2025;15(24):3247. doi:10.3390/diagnostics15243247
  18. Roy R, Chopra A, Karmakar S, Bhat SG. Applications of artificial intelligence (AI) for diagnosis of periodontal/peri-implant diseases: a narrative review. J Oral Rehabil. 2025;52(8):1193-1219. doi:10.1111/joor.14045
  19. Yauney G, Rana A, Wong LC, Javia P, Muftu A, Shah P. Automated process incorporating machine learning segmentation and correlation of oral diseases with systemic health. Annu Int Conf IEEE Eng Med Biol Soc. 2019;2019:3387-3393. doi:10.1109/EMBC.2019.8857965
  20. Feres M, Louzoun Y, Haber S, Faveri M, Figueiredo LC, Levin L. Support vector machine-based differentiation between aggressive and chronic periodontitis using microbial profiles. Int Dent J. 2018;68(1):39-46. doi:10.1111/idj.12326
  21. Balaei AT, de Chazal P, Eberhard J, Domnisch H, Spahr A, Ruiz K. Automatic detection of periodontitis using intra-oral images. Annu Int Conf IEEE Eng Med Biol Soc. 2017;2017:3906-3909. doi:10.1109/EMBC.2017.8037710
  22. da Mata Santos RP, Vieira Oliveira Prado HE, Soares Aranha Neto I, et al. Automated identification of dental implants using artificial intelligence. Int J Oral Maxillofac Implants. 2021;36(5):918-923. doi:10.11607/jomi.8684
  23. Oh S, Kim YJ, Kim J, et al. Deep learning-based prediction of osseointegration for dental implant using plain radiography. BMC Oral Health. 2023;23(1):208. doi:10.1186/s12903-023-02921-3
  24. Fontenele RC, Gerhardt MDN, Picoli FF, et al. Convolutional neural network-based automated maxillary alveolar bone segmentation on cone-beam computed tomography images. Clin Oral Implants Res. 2023; 34(6):565-574. doi:10.1111/clr.14063
  25. Sakai T, Li H, Shimada T, et al. Development of artificial intelligence model for supporting implant drilling protocol decision making. J Prosthodont Res. 2023;67(3):360-365. doi:10.2186/jpr.JPR_D_22_00053
  26. Morgan N, Van Gerven A, Smolders A, de Faria Vasconcelos K, Willems H, Jacobs R. Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images. Sci Rep. 2022;12(1):7523. doi:10.1038/s41598-022-11483-3
  27. Kwak GH, Kwak EJ, Song JM, et al. Automatic mandibular canal detection using a deep convolutional neural network. Sci Rep. 2020; 10(1):5711. doi:10.1038/s41598-020-62586-8
  28. Huang N, Liu P, Yan Y, et al. Predicting the risk of dental implant loss using deep learning. J Clin Periodontol. 2022;49(9):872-883. doi:10.1111/jcpe.13689
  29. Eroğlu Çakmakoğlu E, Günay A. Dental students' opinions on use of artificial intelligence: a survey study. Med Sci Monit. 2025;31:e947658. doi:10.12659/MSM.947658
  30. Hamd Z, Elshami W, Al Kawas S, Aljuaid H, Abuzaid MM. A closer look at the current knowledge and prospects of artificial intelligence integration in dentistry practice: a cross-sectional study. Heliyon. 2023;9(6):e17089. doi:10.1016/j.heliyon.2023.e17089