Perceptions and attitudes of medical students toward AI in anatomy learning

Artificial intelligence (AI) tools are increasingly being integrated into medical education. However, their use in anatomy learning, particularly among medical students in the Middle East, remains underexplored.

To assess medical students' perceptions, attitudes, and patterns toward AI use for anatomy learning, and to examine associations with sex, academic level, and GPA.

A cross-sectional study was conducted among medical students at Jordan University of Science and Technology. A validated 36-item questionnaire (Cronbach's α = 0.785-0.867) was distributed online. Responses were analyzed using descriptive and inferential statistics, including chi-square tests and Spearman's rank correlation, to identify factors associated with AI use and perceptions of AI in anatomy learning.

Among 351 respondents, 300 (85.5%) use AI for anatomy, with 95.7% of them using it during exam preparation. ChatGPT was the dominant platform (96.0%). Gross anatomy was the most studied topic. Students strongly endorsed AI platforms for understanding concepts, immediate answers, 24/7 availability, and time-saving. Major concerns included a lack of visual/lab experience, premium costs, and AI misunderstanding questions. Overall, among the AI-using subgroup, 75.3% of students supported the formal integration of AI, but 52.3% believed AI could replace aspects of traditional teaching. Two gender-related patterns were observed in the unadjusted analyses, but neither remained statistically significant after FDR correction. Higher academic performance was associated with lower agreement that AI could replace traditional teaching or function as a standalone learning resource.

Medical students use AI as an anatomy learning adjunct, valuing its conceptual and accessibility benefits while recognizing its inability to replace hands‑on dissection. Higher GPA was only associated with lower agreement that AI could serve as a complete standalone resource for anatomy learning. These findings support structured AI literacy training, equitable access, and balanced curricula AI integration that preserves hands-on foundations.