Beyond Gender Disparities in Artificial Intelligence Acceptance and Integration in Healthcare Education: A Cross-sectional Study

Abstract Background: Artificial intelligence (AI) is increasingly being integrated into healthcare education, limited research exists on gender-specific differences in AI acceptance and utilization among healthcare students. Aims: This study assessed gender disparities in AI acceptance, utilization patterns, and perceived educational impact among undergraduate students. Materials and Methods: A cross-sectional survey was conducted among 400 undergraduate students (209 females and 191 males) from the pharmacy and optometry programs at a large public university during the 2023–2024 academic year. Data were collected using a validated online questionnaire that asked about demographics, AI technology experience, perceptions of AI integration, challenges, preferences, and future implications. Results: Female students demonstrated significantly higher prior experience with AI technology (91.39% vs. 83.25%, P = 0.014) and reported more frequent use of AI tools in classes ( P = 0.001). Females exhibited higher comfort levels with AI ( P = 0.046) and greater satisfaction with AI integration ( P = 0.037). Despite reporting more technical challenges (67.46% vs. 56.54%, P = 0.024), female students were more optimistic about AI shaping healthcare education’s future ( P = 0.020). Multivariable regression analysis revealed that the frequency of AI use (odds ratio [OR] = 2.00, P < 0.001) and comfort level with AI (OR = 2.79, P < 0.001) were significant predictors of satisfaction, whereas gender was not a significant predictor in any regression model (all P > 0.05). Conclusions: While significant gender differences exist in AI experience, usage patterns, and perceptions among healthcare students, these differences do not translate into differential outcomes when controlling for other factors. The findings suggest that comfort level and frequency of use are more important predictors of AI satisfaction than gender itself. Educational institutions should focus on enhancing AI comfort and accessibility for all students while addressing the specific technical challenges reported by different demographic groups.