Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Exploring medical students’ attitudes and perceptions toward artificial intelligence in medicine in Shandong Province, China
2
Zitationen
8
Autoren
2025
Jahr
Abstract
BACKGROUND: The integration of artificial intelligence (AI) into medical education has transformative potential, yet understanding medical students' attitudes toward AI remains critical for its effective implementation. This study investigates the attitudes, perceptions, and the factors influencing them among medical students in Shandong, China, toward AI in education. METHODS: A cross-sectional survey was conducted from May to June 2025 involving medical students at five medical universities in Shandong, China. Employing convenience sampling, 788 validated participants completed questionnaires that assessed variables including AI familiarity, perceived usefulness (PU), perceived ease of use (PEU), and ethical concerns. Statistical analyses comprised descriptive statistics, independent t-tests and one-way ANOVA tests. RESULTS: Based on 788 valid responses, the study revealed high levels of both familiarity with and usage of AI tools among medical students (47.33% and 91.24%). While they hold positive perceptions of AI's PU (3.60 ± 0.85) and PEU (3.66 ± 0.76), significant ethical concerns exist, including privacy issues (48.48%), fears of eroding critical thinking (61.93%), and academic integrity worries (55.84%). Male students (p = 0.020) and those in higher academic years (p < 0.001) demonstrated stronger AI competency, with ethical apprehensions increasing notably as students' progress through their medical education (p < 0.001). Institutional affiliation had little impact on these patterns (p > 0.05). CONCLUSIONS: Shandong medical students demonstrate cautious optimism regarding AI adoption, recognizing its educational potential while emphasizing the necessity of ethical frameworks and operational safeguards. Curriculum adaptations and transparent governance mechanisms should be implemented to ensure congruence between technological implementation and educational objectives.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.674 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.583 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.105 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.862 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.