Semantic Scholar Open Access 2023 91 sitasi

AI in medical education: medical student perception, curriculum recommendations and design suggestions

Qianying Li Yunhao Qin

Abstrak

Medical AI has transformed modern medicine and created a new environment for future doctors. However, medical education has failed to keep pace with these advances, and it is essential to provide systematic education on medical AI to current medical undergraduate and postgraduate students. To address this issue, our study utilized the Unified Theory of Acceptance and Use of Technology model to identify key factors that influence the acceptance and intention to use medical AI. We collected data from 1,243 undergraduate and postgraduate students from 13 universities and 33 hospitals, and 54.3% reported prior experience using medical AI. Our findings indicated that medical postgraduate students have a higher level of awareness in using medical AI than undergraduate students. The intention to use medical AI is positively associated with factors such as performance expectancy, habit, hedonic motivation, and trust. Therefore, future medical education should prioritize promoting students’ performance in training, and courses should be designed to be both easy to learn and engaging, ensuring that students are equipped with the necessary skills to succeed in their future medical careers.

Topik & Kata Kunci

Penulis (2)

Q

Qianying Li

Y

Yunhao Qin

Format Sitasi

Li, Q., Qin, Y. (2023). AI in medical education: medical student perception, curriculum recommendations and design suggestions. https://doi.org/10.1186/s12909-023-04700-8

Akses Cepat

Lihat di Sumber doi.org/10.1186/s12909-023-04700-8
Informasi Jurnal
Tahun Terbit
2023
Bahasa
en
Total Sitasi
91×
Sumber Database
Semantic Scholar
DOI
10.1186/s12909-023-04700-8
Akses
Open Access ✓