Attitude, perception and barriers of dental professionals towards artificial intelligence
Open accessAbstract
Aim: To know attitudes, perceptions and barriers towards the use of Artificial Intelligence (AI) in dentistry in India among undergraduate and postgraduate students. Methodology: A questionnaire-based cross-sectional study was conducted among participants pursuing graduation and postgraduation. The questionnaire consisted of 23 close-ended and 2 open-ended questions divided into various sections of attitude, perception and barriers. The data was analysed using Statistical Package for Social Sciences (SPSS) version 24.0. Result: Out of 937 responses, 55.2% responded that they get information about AI from social media platforms. 51.3% of respondents have basic knowledge about the use of AI in dentistry. 59.6% agreed that AI can be used as a "definitive diagnostic tool" in the diagnosis of diseases. 66.5% agreed that AI can be used for radiographic diagnosis of tooth caries. 71.3% stated that AI can be used as a "treatment planning tool" in dentistry. 55.7% stated that AI should be part of undergraduate dental training. Conclusion: This study concluded that both dental students are aware of the concept of AI. Participants were positive when asked if AI can increase the efficiency of diagnosis, prognosis and treatment planning procedures as well as in managing patient data. Both participants believed that the barriers to the introduction of AI in dentistry are a lack of technical resources and a lack of training personnel in college.
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Lack of knowledge is an important barrier to the use of AI.
“Other reason is that lack of knowledge is an important barrier to the use of AI [42].”
From Medical, dental, and nursing students’ attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis (2024) · cites this paper as [42]
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Cites
Artificial Intelligence in Dentistry: Chances and Challenges
Schwendicke, F., Samek, W., & Krois, J. (2020) · Journal of Dental Research · doi:10.1177/0022034520915714 · Cited by 1,446 · see citing works
The limitations of online surveys
Andrade, C. (2020) · Indian Journal of Psychological Medicine · doi:10.1177/0253717620957496 · Cited by 618 · see citing works
Artificial intelligence in dentistry: current applications and future perspectives.
Chen, Y., Stanley, K., & Att, W. (2020) · PubMed · doi:10.3290/j.qi.a43952 · Cited by 263 · see citing works
Caries Detection on Intraoral Images Using Artificial Intelligence
Kühnisch, J., Meyer, O., Hesenius, M., Hickel, R., & Gruhn, V. (2021) · Journal of Dental Research · doi:10.1177/00220345211032524 · Cited by 253 · see citing works
Artificial Intelligence in Endodontics: Current Applications and Future Directions
Aminoshariae, A., Kulild, J., & Nagendrababu, V. (2021) · Journal of Endodontics · doi:10.1016/j.joen.2021.06.003 · Cited by 230 · see citing works
Clinically applicable artificial intelligence system for dental diagnosis with CBCT
Ezhov, M., Gusarev, M., Golitsyna, M., Yates, J. M., Kushnerev, E., Tamimi, D., Aksoy, S., Shumilov, E., Sanders, A., & Orhan, K. (2021) · Scientific Reports · doi:10.1038/s41598-021-94093-9 · Cited by 187 · see citing works
Present and future of artificial intelligence in dentistry
Tandon, D., Rajawat, J., & Banerjee, M. (2020) · Journal of Oral Biology and Craniofacial Research · doi:10.1016/j.jobcr.2020.07.015 · Cited by 173 · see citing works
Deep-learning approach for caries detection and segmentation on dental bitewing radiographs
Bayrakdar, İ. Ş., Orhan, K., Akarsu, S., et al. (2021) · Oral Radiology · doi:10.1007/s11282-021-00577-9 · Cited by 132 · see citing works
Promises and perils of artificial intelligence in dentistry
Pethani, F. (2020) · Australian Dental Journal · doi:10.1111/adj.12812 · Cited by 116 · see citing works
Adopting artificial intelligence in dental education: A model for academic leadership and innovation
Islam, N., Laughter, L., Sadid‐Zadeh, R., Smith, C. S., Dolan, T. A., Crain, G., & SQUARIZE, C. H. (2022) · Journal of Dental Education · doi:10.1002/jdd.13010 · Cited by 94 · see citing works
Sur, J., Bose, S., Khan, F., Dewangan, D., Sawriya, E., & Roul, A. (2020) · Imaging Science in Dentistry · doi:10.5624/isd.2020.50.3.193 · Cited by 89 · see citing works
Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs
Mao, Y.-C., Chen, T., Chou, H.-S., et al. (2021) · Sensors · doi:10.3390/s21134613 · Cited by 89 · see citing works
Federated Learning in Dentistry: Chances and Challenges
Rischke, R., Schneider, L., Müller, K. R., Samek, W., Schwendicke, F., & Krois, J. (2022) · Journal of Dental Research · doi:10.1177/00220345221108953 · Cited by 67 · see citing works
Machine learning in orthodontics: Challenges and perspectives
Liu, J., Chen, Y., Li, S., et al. (2021) · Advances in Clinical and Experimental Medicine · doi:10.17219/acem/138702 · Cited by 66 · see citing works
Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review
AbuSalim, S., Zakaria, N., Islam, M. R., Kumar, G., Mokhtar, N. B., & Abdulkadir, S. J. (2022) · Healthcare · doi:10.3390/healthcare10101892 · Cited by 65 · see citing works
Guided innovations: Robot-assisted dental implant surgery
Rawal, S. (2022) · Journal of Prosthetic Dentistry · doi:10.1016/j.prosdent.2022.03.029 · Cited by 57 · see citing works
Application of Machine Learning to Stomatology: A Comprehensive Review
Sun, M., Liu, Y., Liu, G., Cui, D., Heidari, A. A., Jia, W.-Y., Ji, X., Chen, H., & Luo, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3028600 · Cited by 41 · see citing works
Evangelista, K., Silva, B. S. D. F., Yamamoto‐Silva, F. P., Valladares‐Neto, J., Silva, M. A. G., Cevidanes, L., Canto, G. D. L., & Massignan, C. (2022) · Clinical Oral Investigations · doi:10.1007/s00784-022-04742-0 · Cited by 38 · see citing works
Philosophical Logic and Artificial Intelligence
Thomason, R. H. (1989) · Springer eBooks · doi:10.1007/978-94-009-2448-2_1 · Cited by 33 · see citing works
Patient Data-Sharing for AI: Ethical Challenges, Catholic Solutions
Baric-Parker, J., & Anderson, E. E. (2020) · The Linacre Quarterly · doi:10.1177/0024363920922690 · Cited by 32 · see citing works
Bhandari, A., Purchuri, S. N., Sharma, C., Ibrahim, M., & Prior, M. (2021) · Clinical Imaging · doi:10.1016/j.clinimag.2021.08.004 · Cited by 32 · see citing works
Awareness and Education of Medical Students toward Artificial Intelligence and Radiology
Sachdev, R., Garg, K., & Srivastava, A. (2021) · Dentistry and Medical Research · doi:10.4103/dmr.dmr_17_21 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [2]Artificial intelligence and echocardiography - PubMed [cited 2022 Oct 27]. Available from: https://pubmed.ncbi.nlm.nih.gov/30400053/
Cited by
Sorted by citations
Amiri, H., Peiravi, S., rezazadeh shojaee, S. S., Rouhparvarzamin, M., Nateghi, M. N., Etemadi, M. H., ShojaeiBaghini, M., Musaie, F., Anvari, M. H., & Asadi Anar, M. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05406-1 · Cited by 149 · see citing works
“Other reason is that lack of knowledge is an important barrier to the use of AI [42].”
BackgroundDevelopment of the Artificial Intelligence Perception and Attitude Scale (AIPAS)
Dinler, H. (2025) · Bartın University Journal of Faculty of Education · doi:10.14686/buefad.1602673 · Cited by 4 · see citing works
- Authors
- Singh, N., Pandey, A., Tikku, A. P., Verma, P., & Singh, B. P.
- Affiliations
- King George's Medical University (IN)
- Corresponding author
- Singh, Nishi
- Venue
- Journal of Oral Biology and Craniofacial Research
- Publisher
- Elsevier BV
- Published
- 2023-09
- Volume
- 13
- Issue
- 5
- Pages
- 584-588
- ISSN
- 2212-4268 (ISSN-L)
- PMC
- PMC10415790
- OpenAlex
- W4385438120
- Cited by
- 39
- References
- 22
- Citations by year
- 2026: 13 · 2025: 10 · 2024: 15 · 2023: 1
- FWCI
- 1.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Oral Biology and Craniofacial Research (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Indian Council of Medical Research
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Dentistry › Oral Surgery › Dental Radiography and Imaging
- Health Sciences › Health Professions › General Health Professions › Dental Education, Practice, Research
- Keywords
- artificial intelligence in dentistry, dental students, attitudes toward AI, perceptions of AI, AI adoption barriers, AI knowledge, AI-assisted diagnosis, dental education
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