Knowledge and prospects for implementing artificial intelligence among Iraqi dental students: A questionnaire-based survey

Background: Artificial intelligence (AI) in dentistry could improve future clinical practice. Aim: This questionnaire survey was commissioned to evaluate the knowledge and perspective of Iraqi dental students on AI application in dentistry. Methods: The survey was administered to 310 dental students, 112 males and 198 females, ages 19–24. Eighty students were in the 2nd grade, 70 in the 3rd grade, 80 in the 4th grade, and 80 in the 5th grade. They completed questionnaires, selecting one option from the list of possible answers for each of the 11 survey questions about AI. Results: A 42.7% of the 310 participants in the study had experience with AI and its software. However, only 35.3% of dental students understood how to incorporate AI into their work. Students in 5th grade were more likely to think that AI has a future in Iraq (p = 0.034), whereas students in 4th grades rejected the idea of using AI in decision-making (p = 0.045) significantly. Conclusion: Dental students lack a basic grasp of AI; therefore, activating their interest helps them learn about AI and its potential uses. To acquaint dental students with AI and its applications, seminars, lectures, and workshops should coincide with the material they introduce in the curriculum.

Authors

Institutions

Publication Details

Journal
Journal of emergency medicine, trauma & acute care
Published
2024-11-18
DOI
https://doi.org/10.5339/jemtac.2024.midc.11
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Knowledge and prospects for implementing artificial intelligence among Iraqi dental students: A questionnaire-based survey

Nuhad A. Hassan, Asmaa Abed Shandi, Firas H. Alwade
Journal of emergency medicine, trauma & acute care
Artificial Intelligence in Healthcare and Education
article

Knowledge and prospects for implementing artificial intelligence among Iraqi dental students: A questionnaire-based survey

Nuhad A. Hassan, Asmaa Abed Shandi, Firas H. Alwade
article en

Abstract

Background: Artificial intelligence (AI) in dentistry could improve future clinical practice. Aim: This questionnaire survey was commissioned to evaluate the knowledge and perspective of Iraqi dental students on AI application in dentistry. Methods: The survey was administered to 310 dental students, 112 males and 198 females, ages 19–24. Eighty students were in the 2nd grade, 70 in the 3rd grade, 80 in the 4th grade, and 80 in the 5th grade. They completed questionnaires, selecting one option from the list of possible answers for each of the 11 survey questions about AI. Results: A 42.7% of the 310 participants in the study had experience with AI and its software. However, only 35.3% of dental students understood how to incorporate AI into their work. Students in 5th grade were more likely to think that AI has a future in Iraq (p = 0.034), whereas students in 4th grades rejected the idea of using AI in decision-making (p = 0.045) significantly. Conclusion: Dental students lack a basic grasp of AI; therefore, activating their interest helps them learn about AI and its potential uses. To acquaint dental students with AI and its applications, seminars, lectures, and workshops should coincide with the material they introduce in the curriculum.

Journal of emergency medicine, trauma & acute careVol. 2024(8)
Mustansiriyah University (IQ)
Openalex Percentile: Top 81%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.