Perception and Utilization of Artificial Intelligence in Dentistry and Dental Education:A Cross-Sectional Study

Authors

  • Sana Tariq HBS Institute of Healthcare and Allied Health Sciences Islamabad Pakistan
  • Jamilah Riaz Janjua HBS Institute of Healthcare and Allied Health Sciences Islamabad Pakistan
  • Muhammad Riaz Shahbaz Janjua HBS Institute of Healthcare and Allied Health Sciences Islamabad Pakistan
  • Rimsha Shiraz HBS Institute of Healthcare and Allied Health Sciences Islamabad Pakistan
  • Maryam Naseem HBS Institute of Healthcare and Allied Health Sciences Islamabad Pakistan

DOI:

https://doi.org/10.51253/pafmj.v76i1.14200

Keywords:

Artificial intelligence; Dental education; Dental trainees; Academic perception; Utilization

Abstract

Objective: to assess perception, awareness and utilization of artificial intelligence in dental education and dental practice.

Study Design: Cross-sectional survey.

Place and Duration of Study: Online Survey across Pakistan from Mar to Oct 2025.

Methodology: A structured questionnaire was distributed electronically. Assessing demographics, awareness, Al utilization, academic benefits and perceptions regarding the role of Al in dental practice. Data were analyzed using Statistical Package for the Social Sciences (SPSS) version 22:00.

Results: A total of 210 undergraduate dental students and house officers participated in the study. The majority of respondents were familiar with artificial intelligence (86.7%), and 70.5% reported regular use of AI tools. ChatGPT was the most commonly used platform (76.2%), followed by Google Gemini (45.2%) and Grammarly (41.9%). Most participants perceived AI as beneficial in academics, particularly for summarizing learning material (85.7%), writing assignments (81.9%), and improving learning efficiency (80.0%). In clinical practice, a high proportion acknowledged the role of AI in early detection of oral diseases (86.7%), interpretation of digital radiographs (83.8%), and diagnosis (81.0%). Although 89.5% believed AI will play an important role in the future of dentistry, 57.1% expressed ethical concerns, and the majority (65.7%) disagreed that AI could replace teachers.

Conclusion: Dental trainees demonstrated a generally positive perception and increasing utilization of artificial intelligence. Higher AI use was significantly associated with favorable academic perceptions and viewed as a supportive adjunct rather than a substitute for educators.

 

Downloads

Download data is not yet available.

References

1. Schwendicke F, Samek W, Krois J. Artificial Intelligence in Dentistry: Chances and Challenges. J Dent Res 2020 ;99(7):769-774.

https://doi.org/10.1177/0022034520915714

2. England JR, Cheng PM. Artificial Intelligence for Medical Image Analysis: A Guide for Authors and Reviewers. AJR Am J Roentgenol. 2019;212(3):513-519.

https://doi.org/10.2214/AJR.18.20490

3. Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022;28(1):31-38.

https://doi.org/10.1038/s41591-021-01614-0

4. Aldosari B, Alanazi A. Pitfalls of Artificial Intelligence in Medicine. Stud Health Technol Inform. 2024 Aug;316:554-555.

https://doi.org/10.3233/SHTI240474

5. Aldosari B, Aldosari H, Alanazi A. Challenges of Artificial Intelligence in Medicine. Stud Health Technol Inform. 2025 ;323:16-20.

https://doi.org/10.3233/SHTI250039

6. Ueda D, Kakinuma T, Fujita S, Kamagata K, Fushimi Y, Ito R, et al. Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol 2024;42(1):3-15.

https://doi.org/10.1007/s11604-023-01474-3

7. Upadhyay U, Gradisek A, Iqbal U, Dhar E, Li YC, Syed-Abdul S. Call for the responsible artificial intelligence in the healthcare. BMJ Health Care Inform 2023 ;30(1):e100920. https://doi.org/10.1136/bmjhci-2023-100920

8. Mortaheb P, Rezaeian M. Metal Artifact Reduction and Segmentation of Dental Computerized Tomography Images Using Least Square Support Vector Machine and Mean Shift Algorithm. J Med Signals Sens 2016;6(1):1-11.

9. Revilla-León M, Gómez-Polo M, Barmak AB, Inam W, Kan JYK, Kois JC, et al. Artificial intelligence models for diagnosing gingivitis and periodontal disease: A systematic review. J Prosthet Dent 2023;130(6):816-824.

https://doi.org/10.1016/j.prosdent.2022.01.026

10. Setzer FC, Shi KJ, Zhang Z, Yan H, Yoon H, Mupparapu M, Li J. Artificial Intelligence for the Computer-aided Detection of Periapical Lesions in Cone-beam Computed Tomographic Images. J Endod 2020 46(7):987-993.

https://doi.org/10.1016/j.joen.2025.09.022

11. Mohammad-Rahimi H, Motamedian SR, Rohban MH, Krois J, Uribe SE, Mahmoudinia E, Rokhshad R, Nadimi M, Schwendicke F. Deep learning for caries detection: A systematic review. J Dent 2022 ;122:104115.

https://doi.org/10.1016/j.jdent.2022.104115

12. Ramos-Gomez F, Marcus M, Maida CA, Wang Y, Kinsler JJ, Xiong D, et al. Using a Machine Learning Algorithm to Predict the Likelihood of Presence of Dental Caries among Children Aged 2 to 7. Dent J 2021 ;9(12):141.

https://doi.org/10.3390/dj9120141

13. Estai M, Tennant M, Gebauer D, Brostek A, Vignarajan J, Mehdizadeh M, et al. Evaluation of a deep learning system for automatic detection of proximal surface dental caries on bitewing radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol 2022;134(2):262-270.

https://doi.org/10.1016/j.oooo.2022.03.008

14. De-Deus G, Souza EM, Silva EJNL, Belladonna FG, Simões-Carvalho M, Cavalcante DM, et al. A critical analysis of research methods and experimental models to study root canal fillings. Int Endod J 2022;55 Suppl 2:384-445.

https://doi.org/10.1111/iej.13713

15. Chau RCW, Hsung RT, McGrath C, Pow EHN, Lam WYH. Accuracy of artificial intelligence-designed single-molar dental prostheses: A feasibility study. J Prosthet Dent 2024 ;131(6):1111-1117. https://doi.org/10.1016/j.prosdent.2022.12.004

16. Lee DW, Kim SY, Jeong SN, Lee JH. Artificial Intelligence in Fractured Dental Implant Detection and Classification: Evaluation Using Dataset from Two Dental Hospitals. Diagnostics 2021 ;11(2):233.

Downloads

Published

28-02-2026

Issue

Section

Original Articles

Categories

How to Cite

1.
Sana Tariq, Jamilah Riaz Janjua, Muhammad Riaz Shahbaz Janjua, Rimsha Shiraz, Maryam Naseem. Perception and Utilization of Artificial Intelligence in Dentistry and Dental Education:A Cross-Sectional Study. Pak Armed Forces Med J [Internet]. 2026 Feb. 28 [cited 2026 Mar. 5];76(1):130-4. Available from: https://www.pafmj.org/PAFMJ/article/view/14200