ArticleJournal of multidisciplinary healthcare2025
Evaluation of Health Science Students' Knowledge, Attitudes, and Practices Toward Artificial Intelligence in Northern Saudi Arabia: Implications for Curriculum Refinement and Healthcare Delivery.
Article in Journal of multidisciplinary healthcare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Assessing Primary Care Physicians' Readiness for AI-Based Adaptive Learning: Perceptions, Barriers, and Learning Needs in Northern Saudi Arabia.Healthcare (Basel, Switzerland) · 2026Article
- Application of artificial intelligence in medical education: Saudi Arabian perspective.Frontiers in medicine · 2026Review
- Exploring medical students' attitudes and perceptions toward artificial intelligence in medicine in Shandong Province, China.BMC medical education · 2025Article
- Review
- The current status, knowledge, attitudes, and challenges of generative artificial intelligence use among undergraduate nursing students: a single-center cross-sectional survey of western China.Frontiers in public health · 2025Article
- Are ChatGPT, My AI Snapchat, and Metaverse used by dental students as reliable sources of dental education?Frontiers in dental medicine · 2025Article
- Article
- The Impact of Artificial Intelligence Attitudes and Acceptance on Critical Thinking Motivation among Nursing Students in Saudi Arabia.SAGE open nursingArticle
- Review
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Authors and funding
6 authors.
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Abstract
Background and Aim: As the integration of artificial intelligence (AI) in healthcare delivery becomes increasingly prevalent, understanding the knowledge, attitudes, and practices of health science students towards AI is crucial. However, limited evidence exists regarding the readiness of health science students, particularly in northern Saudi Arabia (KSA), to integrate AI into their future practices, highlighting the need for focused evaluation. We evaluated northern Saudi health science students' knowledge, attitude, practice, and associated factors toward AI. Participants and Methods: The present cross-sectional study was conducted among 384 health science students aged 18 years and above from Jouf University, KSA. The study employed a validated data collection form with four sections: demographics, knowledge (AI principles and applications), attitudes (perceptions and ethical concerns), and practices (usage and confidence in AI tools). The three domains' scores were categorized as low (<60%), medium (60-80%) and high (>80%) based on their total scores. We utilized Spearman correlation test to ascertain the strength and direction of correlation among each subscale. Additionally, multivariate analysis was employed to identify associated factors. Results: The present study demonstrated low knowledge, attitude, and practices among 55.7%, 37.0%, and 50.3% of health science students. We observed a positive correlation between knowledge and attitude (rho = 0.451, p = 0.001), knowledge and practice (rho = 0.353, p = 0.001), and attitude and practice (rho = 0.651, p = 0.001). Knowledge (p = 0.001) and practice (p = 0.002) were significantly higher among the students who participated in a formal AI training program. Females had a significantly higher level of attitude (p = 0.001) and practice (p = 0.030) than males. Conclusion: In light of these findings, refining the curriculum to incorporate AI emerges as a critical strategy for addressing gaps in AI knowledge, attitudes, and practices among health science students. Therefore, formal and integrated training programs tailored to suit the local setting can effectively prepare health science students to adopt AI technologies in ways that enhance patient care.
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