BACKGROUND: We assessed AI knowledge, attitudes, and use among medical and dental students in Pakistan, a lower-middle-income country (LMIC), and identified predictors of low AI use within Digital Determinants of Health (DDoH) and Professional Identity Formation (PIF) frameworks. METHODS: A cross-sectional survey of 501 medical and dental students in Khyber Pakhtunkhwa assessed knowledge, attitudes, practices, and barriers related to AI. Analyses used ANOVA and logistic regression for low AI use (score ≤ 15; 7-35 scale, higher = greater use), with sensitivity models adjusted for age, knowledge, attitude scores, and selected barriers. RESULTS: < 0.001). The most significant barriers were a lack of training (71.7%) and limited technical access (69.7%). CONCLUSIONS: AI use was lower in later training years and differed by gender and specialty. The inverse knowledge-practice association contradicts standard adoption models and warrants longitudinal investigation; one PIF-based hypothesis is that higher-knowledge students exercise professional restraint, although cross-sectional data cannot confirm it. The clinical-phase decline implicates ward-based norms as independent barriers. Competency-based training with structured access provisions and faculty development, particularly in clinical phase education, is needed.
Qazi et al. (Mon,) studied this question.