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

NCT06951152 | Active Not Recruiting DMC

• 1 other identifier: FDASU-RECIM012404
• Study Type: observational
• Target: 275
• Locations: 1 country
• Sites: 1

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)

Outcome Measures

Primary Outcomes (1)

• Assessment of knowledge about AI applications in periodontology

  Assessment of knowledge about AI applications in periodontology, Data obtained from the questionnaires will be entered into an Excel spreadsheet to serve as a database. The acquired data will be subjected to statistical analysis using the SPSS software version 23 (SPSS for Windows, Chicago, USA). The Shapiro-Wilk test will be used to assess the data. questionnaire comprised five sections with 33 questions. The knowledge-assessing questions known as the second section, consisted of six closed-ended questions, identifying the basic knowledge of the periodontists participating in AI using a Likert three-point scale (yes/no/maybe).

  1 year

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

The study will be conducted on post graduate periodontists either master or PhD holder.

You may qualify if:

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

You may not qualify if:

• Any undergraduate dental student

Sponsors & Collaborators

• Ain Shams University: lead

Study Sites (1)

Ain Shams University

Cairo, Egypt

Location

Related Publications (1)

• 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

  BACKGROUND

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 4, 2025
• First Posted: April 30, 2025
• Study Start: September 1, 2023
• Primary Completion: September 1, 2024
• Study Completion: September 1, 2025
• Last Updated: April 30, 2025

Record last verified: 2025-03

Locations

EG (1)