Aug 2026· PLOS Digital Health· Vol 5· 0 citations· 15 references
Medicine
TL;DR
Examining the adoption patterns, perceived barriers, and determinants of artificial intelligence use among health sciences students at Palestine Polytechnic University in Palestine found that college affiliation and knowledge score significantly predicted AI adoption, whereas gender, academic year, and previous AI training were not significant predictors.
Abstract
Artificial intelligence (AI) is increasingly integrated into healthcare education worldwide, yet disparities in access, training, and institutional readiness remain evident, particularly in low-resource and conflict-affected settings. Understanding how health sciences students engage with AI technologies and the barriers they encounter is essential for guiding the development of AI-ready curricula in Palestinian universities. This study aimed to examine the adoption patterns, perceived barriers, and determinants of artificial intelligence use among health sciences students at Palestine Polytechnic University in Palestine. A descriptive cross-sectional study was conducted among 666 undergraduate students from the Colleges of Nursing, Medicine and Health Sciences, and Dentistry. Data were collected using a validated self-administered questionnaire assessing demographic characteristics, AI knowledge, attitudes, practice behaviors, and perceived barriers. Descriptive statistics summarized usage patterns. Mann–Whitney U tests, Kruskal–Wallis tests, and chi-square analyses examined group differences. Multivariate logistic regression identified predictors of AI adoption. Statistical significance was set at p ≤ .05. The result of the study. AI use was highly prevalent, with 93.4% of students reporting active engagement. AI was primarily used for study and learning (87.7%), written assignments (57.5%), and personal purposes (54.2%). Significant differences in AI usage were observed across academic disciplines (χ² = 17.292, p = .008), with dentistry students reporting longer daily use. Major barriers included limited curriculum integration (48.2%), ethical and privacy concerns (47.9%), and insufficient training centers (40.4%). Multivariate analysis showed that college affiliation and knowledge score significantly predicted AI adoption, whereas gender, academic year, and previous AI training were not significant predictors. The Conclusion. Despite widespread exposure to AI technologies, students’ engagement remains largely informal and constrained by curricular, infrastructural, and ethical barriers. Institutional strategies including curriculum reform, faculty development, and improved digital infrastructure are necessary to support responsible AI integration in health sciences education in Palestine.
Background The growing integration of artificial intelligence (AI) into healthcare and medical education has created an urgent need to evaluate how prepared undergraduate students are to engage with these technologies. Medical graduates will increasingly encounter AI-driven tools across clinical and educational settings, yet systematic assessment of their readiness and perceptions remains limited, particularly in India. This study aimed to assess AI readiness and perceptions among undergraduate medical students and to examine how readiness varied in relation to sociodemographic characteristics, prior AI training, and patterns of AI tool utilization. Methods This cross-sectional study enrolled 310 undergraduate Bachelor of Medicine, Bachelor of Surgery (MBBS) students at a tertiary care teaching institution in Surendranagar, Gujarat, between September and November 2025. AI readiness and perception were assessed using the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) and a separately developed, validated 10-item questionnaire, respectively. Descriptive statistics were summarized as means, standard deviations, frequencies, and percentages. Participants were categorized as having poor (≤66), average (67-78), or good (≥79) readiness using cut-offs derived from the 33.33rd and 66.67th percentiles of the observed MAIRS-MS score distribution. The same percentile-based approach was applied to each MAIRS-MS domain score. Pearson's chi-square test or Fisher's exact test, as appropriate, was used to examine associations between categorical variables, while Spearman's rank correlation coefficient was used to assess relationships between readiness scores and selected variables. A two-sided p-value of less than 0.05 was considered statistically significant. Results The mean age of the study participants was 20.03 ± 1.65 years, and 182 (58.71%) were female subjects. Using the 33.33rd and 66.67th percentiles of the observed MAIRS-MS score distribution, 169 of 238 participants (71.0%) were classified as having average AI readiness. Previous exposure to AI training (χ² =6.33, p=0.042) was significantly associated with the ethics domain of AI readiness. Total AI readiness showed extremely weak positive correlations with age (r=0.087, p=0.181) and academic year of study (r=0.057, p=0.381), with neither relationship reaching statistical significance. Among all 310 participants, more than half of the students perceived AI as useful for several educational purposes, including teaching (n=165, 53.23%), assignment preparation (n=162, 52.26%), self-learning (n=166, 53.55%), understanding complex concepts (n=170, 54.84%), and clinical case scenarios (n=161, 51.93%). Substantial proportions also expressed concerns regarding misleading information (n=151, 48.70%), potential effects on clinical skills and critical thinking (n=151, 48.71%), and data privacy (n=133, 42.90%). Conclusion Overall, undergraduate medical students demonstrated an average level of AI readiness and generally mixed-to-positive perceptions toward artificial intelligence, with a considerable proportion of students remaining neutral across several items. Previous AI training or exposure was significantly associated with the ethics domain of AI readiness. An extremely weak positive correlation was observed between the total readiness score and both age and academic year of study.
Kumarjiv K. Shreshthi, Jay H. Nimavat, Milind Makwana et al.· Cureus· 0 citations
Positive correlations were identified among all measured dimensions, with the strongest association observed between hope and adaptation, and the findings indicate that students’ evaluations of artificial intelligence involve interrelated perceptions of knowledge, anxiety, positive expectations, and educational preparation needs.
Background Amid the global race toward intelligent healthcare systems, Saudi Arabia stands at a pivotal moment in its digital health transformation. Understanding how prepared healthcare professionals are to adopt artificial intelligence is essential for shaping successful national strategies. Objectives This study aimed to assess healthcare professionals’ attitudes, perceptions, and intentions toward using AI in clinical practice; examine awareness and actual use; identify key predictors of AI adoption based on the Unified Theory of Acceptance and Use of Technology (UTAUT); and explore the mediating role of institutional support. Methods A cross-sectional survey was conducted among 521 healthcare professionals, including physicians, nurses, administrators, and allied health workers, across Saudi Arabia. The survey assessed awareness, usage, perceived usefulness, ease of use, social influence, facilitating conditions, perceived risks, and the intention to use AI. Data were analyzed using chi-squared tests, multiple regression, and mediation analyses. Results Although AI awareness was remarkably high (89.1%) and optimism toward the future was strong (79.0%), only 51.2% of participants reported actual clinical use of AI, A 37.9 percentage-point awareness–use gap. Two factors consistently stood out as powerful drivers of intention: believing that AI is genuinely useful (B = 0.491, p < .001) and feeling confident in one's ability to use it (B = 0.224, p < .001). Institutional support played an important but mostly indirect role in shaping intentions by enhancing these two beliefs. Social influence had little effect and was negative for nurses, whereas perceived risk did not significantly deter adoption. Despite structural and ethical challenges, intention to use AI remained high (71.6%), A 20.4 percentage-point gap ahead of actual use, indicating that organizational barriers, rather than individual willingness, remain the primary obstacle to AI integration. Conclusion These dynamics provide unique opportunities. With 71.6% of professionals intending to adopt AI, targeted training initiatives, clearer governance frameworks, and organizational support may help facilitate the sustainable integration of AI into healthcare practice. This study offers empirical evidence and a roadmap for transforming enthusiasm into sustainable, safe, and meaningful AI integration, and may support healthcare leaders and policymakers in developing strategies for safe and sustainable AI integration within the Saudi healthcare system. Practical Implications Advancing AI-enabled healthcare in Saudi Arabia requires investment not only in technology, but also in healthcare professionals’ preparedness and organizational support. Structured training programs, hands-on exposure to AI tools, supportive leadership, adequate infrastructure, and clear data governance frameworks may help healthcare professionals adopt AI more confidently and sustainably within clinical practice.
Yara Alrashed, N. Alsaheil· Frontiers in Health Services· 0 citations
Abstract Background AI is increasingly being integrated into education and health care, offering opportunities to improve learning, understanding of clinical cases, and students’ self-confidence. However, it remains necessary to assess nursing students’ perceptions of AI and its impact on their academic and professional development. Objective The aim of the study was to assess AI use among nursing students, their perceptions of AI, and its impact on learning, academic performance, and professional preparation. Methods A descriptive cross-sectional study with analytical components was conducted at the Faculty of Medical Technical Sciences in Elbasan, Albania. Data were collected through a structured questionnaire administered via Google Forms, which assessed AI use and its perceived impact on learning and professional preparation. Data were analyzed using SPSS (version 23.0). Descriptive statistics, chi-square tests for associations between variables (P<.05), and logistic regression to estimate crude odds ratios (CORs) and adjusted odds ratios (AORs) with 95% CIs were used. Results A total of 279 nursing students participated in the study (mean age 22.4, SD 5.7 years), the majority of whom were female (273/279, 97.8%), lived in urban areas (156/279, 55.9%), and were enrolled in the bachelor’s program (198/279, 71%). Overall, 83.9% (234/279) reported using AI, mainly virtual assistants such as ChatGPT or similar tools (183/234, 78.2%). The most common reason was information searching (216/234, 92.3%), followed by studying and understanding lecture content (96/234, 41%). Bivariate analysis showed no significant associations between AI use and residence or study cycle, whereas grade point average (GPA) was significantly associated with AI use. In the multivariable analysis, GPA remained the only independent predictor of AI use. Students with a GPA of 6.0 to 6.9 (AOR 5.55, 95% CI 1.53-20.14; P=.009) and those with a GPA of 8.0 to 8.9 (AOR 5.73, 95% CI 1.26-26.00; P=.02) were significantly more likely to use AI than the reference group. Students perceived AI as having a moderate impact on learning, particularly understanding lectures (mean 2.58, SD 1.10) and exam preparation (mean 2.58, SD 1.02), whereas its impact on self-confidence (mean 1.97, SD 1.17) and empathy (mean 1.90, SD 1.12) was perceived as low. Although AI was considered useful for supporting learning (mean 2.83, SD 1.12), students expressed concerns regarding the reliability of AI-generated information (mean 3.18, SD 1.22), dependence on AI (mean 2.75, SD 1.28), and its impact on critical thinking (mean 2.80, SD 1.18). Conclusions AI is widely used among nursing students, primarily supporting learning and academic performance. However, its impact on professional and interpersonal competencies remains limited. These findings suggest the need for integrating AI into nursing education curricula, with a focus on critical use and the development of students’ professional competencies.
The findings highlight the need for a supportive educational environment with guidance to enable nursing students to use artificial intelligence appropriately and responsibly when needed, particularly among vocational college students and those from socioeconomically disadvantaged backgrounds.
Hui-Ying Fan, Qing Zhou, Lili Deng et al.· BMC Nursing· 0 citations
Students and faculty demonstrate broadly positive attitudes toward AI alongside concerns about safety, surveillance, and workforce displacement andStructured education, practical exposure, and ethics- and governance-oriented teaching may support responsible AI integration in health-professions education and practice.
Hamad Alhamad, Ahmad Alenezi, Musaed Z. Alnaser et al.· Work· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
