Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Medical Students’ Perception Toward Using AI in Medical Education in the Kurdistan Region, Iraq: A Cross-Sectional Study
2
Zitationen
5
Autoren
2024
Jahr
Abstract
Background and aim AI is revolutionizing medical education by offering innovative tools and simulations that augment traditional teaching methods. This study explored the perceptions and expectations of medical students in the Kurdistan region, Iraq, regarding AI integration in medical education. Methods A cross-sectional online survey collected data from 224 medical students over four months. A descriptive analysis was conducted to present the student's attitudes. Results In total, 224 medical students responded to the online survey. The majority of them were female (n=129; 57.6%), while 95 were male (42.4%). Additionally, most of the participants were in stage 4 (54 (24.1%); stage 1, 48 (21.4%); and stage 2, 43 (19.2%). In terms of measuring students' perceptions of AI integration in medical education, 186 (83%) of the students wanted to use smartphones and tablets, and 38 (17%) of them reported wanting hard copies. In addition, 112 (50%) of the medical students considered themselves experts in using AI and 98 (43.8%) did not know exactly what AI was used; however, only a few of them (6.3%) did not use AI. Few patients reported using Manikins instead of real patients (42 (18.8%)), while 140 (62.5%) reported that they could be used but not an alternative. Conclusion While many agree that digital tools and simulations are useful teaching tools, they are frequently viewed as adjunctive approaches. Better integration and training are required for the infrequent use of AI tools in medical education.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.245 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.102 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.468 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.429 Zit.