BMC Med Educ. 2026 Jan 31. doi: 10.1186/s12909-026-08707-9. Online ahead of print.
ABSTRACT
This study investigates health sciences students’ attitudes toward artificial intelligence (AI) and the implications for ethical awareness, clinical decision-making, and public health. A cross-sectional survey was conducted between April 27 and May 15 2025, with 668 students from five departments at Gümüşhane University, employing the validated Artificial Intelligence Attitude Scale, which measures benefits, risks, and use, alongside 12 binary-response items assessing ethical, clinical, and public health judgments. Descriptive statistics, t-tests, ANOVA, and logistic regression analyses were applied. Findings indicate that students perceive AI as highly beneficial (M = 4.05) but also associate it with notable risks (M = 2.52; where lower scores indicate a higher level of perceived risk due to reverse coding). Logistic regression analyses revealed that risk perception (reverse-coded; higher scores indicating lower perceived risk) was the most consistent predictor across all dimensions. Specifically, students with lower perceived risk were significantly more likely to reject concerns regarding patient privacy (OR = 2.55, 95% CI [2.03-3.21], p < 0.001), dismiss the idea that relying on AI instead of human expertise is problematic (OR = 1.57, 95% CI [1.25-1.96], p < 0.001), and reject the notion that AI systems may harm public health (OR = 2.52, 95% CI [1.98-3.20], p < 0.001). While participants endorsed AI’s potential in enhancing patient safety, chronic disease management, and preventive care, they expressed significant concerns about privacy, legal responsibility, and a potential weakening of patient-clinician communication. Gender, academic discipline, and prior AI use further differentiated attitudes. The results highlight a dual perception of AI as both an opportunity and a threat, emphasizing that successful integration in healthcare requires not only technical competence but also ethical, legal, and communicative safeguards.
PMID:41620691 | DOI:10.1186/s12909-026-08707-9
