JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) is increasingly integrated into healthcare, yet research on the factors influencing its adoption in medical schools remains limited.

Objective:

This study aimed to examine the roles of perceived benefits, perceived risks, and AI literacy in predicting AI use intention among medical students and faculty.

Methods:

A cross-sectional survey was conducted among 141 students and 94 faculty members (N=235) at a single medical school. The survey measured AI perceptions (perceived benefits, risks, and use intention), AI literacy (access, understanding, judgment, and application), and AI-related characteristics (use frequency, knowledge level, and familiarity). Group differences were controlled for sex, age, and AI education experience using analysis of covariance (ANCOVA). Multiple regression analyses identified predictors of AI use intention within each group.

Results:

Perceived benefits were the strongest predictor of AI use intention in both students (β = 0.590, P < .001) and faculty (β = 0.503, P < .001). Among students, perceived risks were also a significant positive predictor (β = 0.192, P = .006), while AI literacy showed a negative association (β = −0.163, P = .025). For faculty, AI literacy (β = 0.200, P = .033) and frequency of AI use (β = 0.257, P = .020) were additional significant positive predictors.

Conclusions:

Perceived benefits drive AI adoption in medical settings. AI literacy’s differing effects call for context-specific, experiential curricula focused on practical application rather than knowledge alone.