Intention to Adopt Artificial Intelligence in Healthcare: Insights from Health Science Students through the Technology Acceptance Model

Abstract

This study aimed to evaluate health science students’ intentions to adopt artificial intelligence (AI) in healthcare and to identify key predictors of these intentions using the Technology Acceptance Model (TAM). A cross-sectional survey was conducted among 967 students enrolled in various health-related disciplines at a university. Data were collected through a self-administered online questionnaire comprising five sections: demographic information, information technology (IT) skills, perceptions of AI in medicine, attitudes towards AI and intentions to adopt AI in healthcare. Approximately half of the participants reported high intentions to integrate AI into their future medical practice. Linear regression analyses confirmed the applicability of the TAM in predicting AI adoption intentions and identified the following three significant predictors: attitudes towards AI, perceptions of AI in medicine and IT skills. Among these, attitudes towards AI emerged as the strongest predictor (β = 0.563, t = 17.687, p < 0.001). These findings underscore the importance of fostering students’ IT competencies and positive perceptions and attitudes to facilitate the effective integration of AI into medical education and practice. Future research should employ longitudinal designs to examine changes in students’ perceptions and attitudes over time, thereby providing insights for curriculum development that better aligns with students’ evolving learning needs and ensures professional readiness.