Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Embracing Artificial Intelligence: Evaluating Technological Adaptability in Palestinian Medical Education
1
Zitationen
8
Autoren
2025
Jahr
Abstract
<title>Abstract</title> Background Artificial intelligence (AI) enables computers to process data and solve problems via algorithms, with China at the forefront of medical AI applications like diagnostics. Medical students increasingly rely on AI tools (e.g., ChatGPT) for education, and ML advances predictive research. Methods A cross-sectional study assessed AI readiness among 799 medical students from all universities in the West Bank, Palestine, that have a Faculty of Medicine, using the validated MAIRS-MS scale (22 items across 4 domains). Data collection combined electronic and paper questionnaires, ensuring high participation and reliability (α = 0.87). Results Most participants were from Hebron University (66%) and represented all academic years. The majority (83%) were aware of AI in medicine, and 73% had prior experience with AI tools. The median total readiness score was 73 (IQR: 66–84), with highest scores in ability (median: 27) and cognition (median: 26), and lower scores in vision and ethics (both median: 10). Males, older students, high-GPA achievers, and those from higher-income backgrounds had significantly higher readiness scores (p < 0.001). Prior AI experience and awareness were also strongly associated with increased readiness. No significant differences were observed across universities or academic years. Conclusion Palestinian medical students demonstrate moderate to high readiness for AI integration in medicine, particularly in technical and cognitive domains. However, notable gaps remain in ethical understanding and visionary thinking. Addressing these gaps requires national curricular reform focused on ethics, regulation, and strategic AI applications. Equitable access to AI education across socioeconomic and gender lines is essential to prepare future physicians for a digitally enhanced healthcare landscape.
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 · 8.100 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.466 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.