Knowledge, Perception, Usage And Concerns Of Artificial Intelligence Applications In Periodontology

NCT ID: NCT06951152

Last Updated: 2025-04-30

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Total Enrollment: 275 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-09-01
• Study Completion Date: 2025-09-01

Brief Summary

Statement of problem: knowledge gap about knowledge, perception ,usage and concerns of artificial intelligence applications in periodontology among periodontists.

Aim of the study:

To investigate ' periodontists' knowledge, perception , usage and concerns towards AI systems' applications in periodontology.

Materials and Methods This will be done by a self-administered, 33-item questionnaire . The questionnaire is divided into five sections.The first section, known as Part A, focus on five open-ended questions on sociodemographic characteristics, where participants enter their age, gender, academic affiliation. Part B consists of closed-ended questions, identifying the basic knowledge of the periodontists participating in AI using a Likert three-point scale (yes / no / maybe) . Part C consists of questions assessing the perception of periodontists towards the use of AI using a Likert three-point scale (yes / no / maybe). Part D consists of questions focusing on the usage of AI applications . part E consists of questions assessing concerns of AI applications in periodontology using a Likert three-point scale (yes / no / maybe) .

This study will be conducted in accordance with the code of ethics of the research ethics committee at the faculty of dentistry, at Ain Shams University. This survey aims to assess the knowledge, perception , usage and concerns of AI applications among periodontists.

The questionnaire will be distributed to periodontologists in the faculty of dentistry at Ain Shams University. Participants will be voluntary and anonymous. The questionnaire consists of five parts and the average time to complete the questionnaire is 10-12 min

Conditions

AI (Artificial Intelligence)

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: CROSS_SECTIONAL

Eligibility Criteria

Inclusion Criteria

• All participants should be post-graduate periodontists.
• Periodontists at Egypt

Exclusion Criteria

• Any undergraduate dental student

• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Ain Shams University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Ain Shams University

Cairo, , Egypt

Countries

Egypt

References

Amato F., López A., Peña-Méndez E.M., Vaňhara P., Hampl A., Havel J. Artificial Neural Networks in Medical Diagnosis. J. Appl. Biomed. 2013;11:47-58. doi: 10.2478/v10136-012-0031-x. - DOI Ayad N, Schwendicke F, Krois J, van den Bosch S, Bergé S, Bohner L, Hanisch M, Vinayahalingam S. Patients' perspectives on the use of artificial intelligence in dentistry: a regional survey. Head Face Med. 2023 Jun 22;19(1):23. doi: 10.1186/s13005-023-00368-z. PMID: 37349791; PMCID: PMC10288769. Bennett, C.C.; Hauser, K. Artificial Intelligence Framework for Simulating Clinical Decision-Making: A Markov Decision Process Approach. Artif. Intell. Med. 2013, 57, 9-19. Briganti, G.; Le Moine, O. Artificial Intelligence in Medicine: Today and Tomorrow. Front. Med. 2020, 7, 27 Cervino, G.; Cicciu, M.; Fiorillo, L.; Finocchio, G. Clinical Applications of the Algorithm "Pipeline Advanced Contrast Enhancement (Pace)" in Dental Radiology. Eng. Proc. 2023, 31, 10 Davenport T, Kalakota R: The potential for artificial intelligence in healthcare. Future Healthc J. 2019, 6:94-. 10.7861/futurehosp.6-2-94 Jiang F, Jiang Y, Zhi H, et al.: Artificial intelligence in healthcare: past, present and future. Stroke VascNeurol. 2017, 2:230-43. 10.1136/svn-2017-000101 Kansal R, Bawa A, Bansal A, Trehan S, Goyal K, Goyal N, Malhotra K. Differences in Knowledge and Perspectives on the Usage of Artificial Intelligence Among Doctors and Medical Students of a Developing Country: A Cross-Sectional Study. Cureus. 2022 Jan 19;14(1):e21434. doi: 10.7759/cureus.21434. PMID: 35223222; PMCID: PMC8860704. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D: Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019, 17:195. 10.1186/ Kolachalama VB, Garg PS: Machine learning and medical education. NPJ Digit Med. 2018, 1:54.10.1038/s41746-018-0061-1 Kooli, C. Chatbots in Education and Research: A Critical Examination of Ethical Implications and Solutions. Sustainability 2023,15, 5614

Reference Type: BACKGROUND

Other Identifiers

FDASU-RECIM012404

Identifier Type: -

Identifier Source: org_study_id