Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Artificial Intelligence in medical education: A comparative study between faculty members and medical students in Sohag University, Egypt
0
Zitationen
3
Autoren
2024
Jahr
Abstract
Introduction: Artificial Intelligence (AI) is creating a revolution and gain a tremendous importance in nearly all fields. AI is a system that uses human abilities as intelligence in analysing the surrounding environment to make decisions and achieve specific goals. The objective of the current study was to evaluate the attitude towards AI and perception of barriers of AI implementation as well as factors associated with them among medical students and medical staff. Methods: A comparative cross-sectional study was conducted among medical students and staff of Sohag University using self-filled online questionnaire. A web-based survey using Google Forms was distributed to participants through social media platforms (WhatsApp, Facebook, Messenger) and university e-mails. Results: A total of 796 participants completed the questionnaire of whom 484 were medical students. Their mean age was 20.9 ± 1.9 for students and 39 ± 8 for medical staff. Females represented 41.5% of students and 75.3% of staff. 17.4% and 13.8% of medical students and staff were proficient at computer technology. Most of the participants reported neutral (58.7%) to positive (32%) attitude towards AI. More than 70% of participants believed that need of appropriate infrastructure, the need for training faculty members and medical students on AI applications and high cost are barriers for AI implementation. Conclusions: As AI applications are becoming widely prevalent, efforts should be directed to incorporate AI in the educational curriculum and train students and staff regarding AI and its applications to prepare them to keep pace with the rapidly changing world.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.402 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.270 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.702 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.507 Zit.