ArticleBMC medical education2026
Health sciences students' attitudes toward artificial intelligence: predictors of ethical awareness, clinical decision-making, and public health perceptions-a cross-sectional study.
Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Applications of Artificial Intelligence in the Health Sector: A PRISMA-Based Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.