Attitudes, usage patterns, and learning interests of medical students toward DeepSeek in medical education: A cross-sectional survey

The integration of artificial intelligence (AI) tools like DeepSeek into scientific research offers new opportunities for efficiency and innovation. However, attitudes and adoption among medical students remain underexplored. This study aims to investigate medical students' attitudes, usage patterns, and interest in learning with DeepSeek. A cross-sectional online survey was conducted among medical students from various academic levels and fields. The questionnaire assessed demographics, attitudes, usage frequency and purposes, and learning interests. Data were analyzed using descriptive statistics and independent sample t-tests. Among 589 respondents, most were familiar with DeepSeek (86.08%), and 70.29% used it. A majority held positive attitudes toward DeepSeek, agreeing that it is useful for academic success (77.91%), makes research easier (78.37%), and will play an important future role (74.51%). However, concerns included reliability (61.34%), overreliance (48.51%), and privacy risks (51.88%). Usage varied: 28.23% used DeepSeek frequently, while 29.76% had never used it. Common uses included problem-solving (406 users) and literature search (256 users). Over 80% expressed interest in learning to use DeepSeek more effectively. No significant differences in attitudes were found across demographic groups. Medical students view DeepSeek as a promising tool for academic and research support but have significant concerns regarding reliability and ethical use. There is strong interest in structured learning opportunities. Integrating AI literacy into medical education and providing targeted training are recommended to promote responsible and effective use.