ArticleDigital health
Nursing students' trust in artificial intelligence (AI) clinical recommendations: A multicenter cross-sectional study of risk-benefit perceptions across Saudi Arabian universities.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- AI Use, Perceptions, and Perceived Impact Among Nursing Students: Cross-Sectional Study.JMIR nursing · 2026Article
- Knowledge, Attitude, Benefits, Risks, Barriers, Professional Impact, and Preparedness of Nursing Students Toward the Utilization of Artificial Intelligence in Healthcare.Nursing reports (Pavia, Italy) · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) is increasingly embedded in clinical decision-making, making trust in AI-generated recommendations essential for safe and effective healthcare delivery. Nursing students, as future practitioners, are critical stakeholders in this transition; however, their trust in AI within rapidly digitizing educational and clinical contexts in Saudi Arabia remains underexplored, despite national digital health initiatives aligned with Vision 2030. Objective: This study examined nursing students' trust in AI-driven clinical recommendations across Saudi universities and investigated how perceived risks, perceived benefits, AI education, and academic progression influence trust development. Methods: A multicenter cross-sectional survey was conducted between September and November 2025 across five Saudi nursing institutions (three all-male, one all-female, and one mixed-gender university). A total of 622 undergraduate nursing students completed validated instruments measuring functional and ethical trust, perceived benefits, perceived risks, applicability, and barriers to AI learning. Multiple linear regression and moderation analyses were performed using robust standard errors. Results: Although students reported highly positive attitudes toward AI (mean = 3.94/5.00) and strong perceived benefits (mean = 3.98/5.00), overall trust in AI-generated clinical recommendations was low (mean = 2.28/5.00), indicating a trust-optimism paradox. Perceived risks positively predicted trust (β = 0.136, Conclusions: Nursing students' trust in AI is dynamic and influenced by educational exposure and clinical experience. Transparent, structured AI education integrating technical and ethical dimensions is associated with calibrated trust and support for responsible AI adoption in clinical practice. These findings have direct implications for nursing education policy, suggesting that integrating transparent, evidence-based AI curricula into digital health education frameworks can support workforce readiness and responsible technology adoption aligned with national healthcare transformation initiatives.
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