Occupational Physicians' Knowledge and Attitudes Regarding the Use of Artificial Intelligence in Occupational Health: A Cross-Sectional Study in Egypt.
Jul 2026· Journal of Occupational and Environmental Medicine· 0 citations
Medicine
TL;DR
Positive attitudes coexist with knowledge gap; structured education and training are needed for effective AI integration in occupational health.
Abstract
Objective
To assess Egyptian occupational physicians' knowledge and attitudes regarding Artificial Intelligence (AI) in occupational health and factors associated with them.
Methods
A cross- sectional study was conducted among 127 occupational physicians, conveniently selected from six Egyptian universities, using structured online questionnaire on demographics, professional experience, and AI-related knowledge and attitudes. Logistic regression was used to identify independently associated factors with both outcomes.
Results
61.4% of participants had poor knowledge, 74.0% reported positive attitudes, and 31.5% perceived AI as a threat. Attendance at AI webinars or courses (AOR=11.13; 95% CI:2.7-46.9) and longer work experience (AOR=1.16; 95% CI:1.01-1.34) were independently associated with good knowledge.
Conclusions
Positive attitudes coexist with knowledge gap; structured education and training are needed for effective AI integration in occupational health. Findings should be interpreted considering non-random sampling and self-reported measures.
The weak negative correlation between knowledge and attitudes suggests that greater awareness of AI may be accompanied by increased concerns regarding its use, and further educational initiatives are needed to enhance nurses’ preparedness for AI integration in clinical practice.
R. Elsayed, A. Nagy, Eman Sobhy El-Said Hussein et al.· BMC Nursing· 1 citation
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
This study examined vocational health services students’ perceived knowledge, anxiety, hope, adaptation, and perspectives regarding the use of artificial intelligence in healthcare and evaluated whether these scores differed according to selected individual and health-related characteristics. A cross-sectional, descriptive, and correlational design was used. The study included 174 students enrolled at Batman University Vocational School of Health Services during the 2025–2026 academic year. Data were collected between April and May 2026 using a Personal Information Form and the Knowledge, Attitudes, and Perspectives Scales on the Use of Artificial Intelligence in Healthcare. Descriptive statistics, independent-samples t tests, Pearson correlation analysis, and Cronbach’s alpha coefficients were used. Internal consistency coefficients ranged from 0.777 to 0.897. No statistically significant differences were found according to gender, year of study, chronic illness status, or living arrangement. Students who regularly used medication had higher anxiety scores in the unadjusted analysis; however, this difference did not remain significant after Holm adjustment and was considered exploratory. Positive correlations were identified among all measured dimensions, with the strongest association observed between hope and adaptation. The findings indicate that students’ evaluations of artificial intelligence involve interrelated perceptions of knowledge, anxiety, positive expectations, and educational preparation needs. Healthcare education programs should include structured content addressing the clinical applications, limitations, ethical implications, and critical evaluation of artificial intelligence.
BACKGROUND
Artificial intelligence (AI) has emerged as one of the most rapidly evolving technologies in recent years and is increasingly being integrated into healthcare, education, and everyday life. Examining university students' attitudes toward this technology is important for understanding their future professional orientations and adaptation to technological change. This study aimed to identify medical students' attitudes toward artificial intelligence and the determinants of these attitudes, and to develop educational implications for medical training based on the findings.
METHODOLOGY
This descriptive cross-sectional study was conducted between January 22 and May 28, 2025, among 198 final-year medical students at Pamukkale University Faculty of Medicine in Denizli, Türkiye. Data were collected using a Descriptive Information Form, which assessed students' sociodemographic characteristics and their knowledge and experiences regarding AI, and the General Attitudes toward Artificial Intelligence Scale (GAAIS, Turkish version). Since the negative subscale is reverse-coded, higher scores in both indicate more positive attitudes toward AI. Data were analyzed using SPSS v25 with descriptive statistics, Mann-Whitney U, Kruskal-Wallis, and multiple linear regression analyses.
RESULTS
The mean age of participants was 24.47 ± 0.98 years; 56.1% were female. A total of 74.2% of the participants reported general knowledge about AI, and 76.3% reported experience using AI in daily life. A total of 63.1% viewed AI developments positively, 69.7% believed AI changes work and daily life, and 52.0% felt emotionally unaffected by it. The mean positive attitude score was 45.01 ± 9.17, and the negative attitude score was 26.41 ± 6.22. Multiple linear regression analysis showed that positive attitudes toward artificial intelligence were significantly associated with father's education level (university vs. primary school), having an interest in technology, perceptions regarding developments in artificial intelligence, and the belief that AI has an emotional impact (p < 0.05). For the negative attitude subscale, only perceptions regarding developments in artificial intelligence were found to be significantly associated (p < 0.05).
CONCLUSION
Medical students demonstrated generally positive attitudes toward AI. Higher paternal education, technological interest, perceiving AI as emotionally influential, and evaluating AI developments positively were predictors of favorable attitudes. In addition, more positive evaluations of AI-related developments were also associated with higher scores on the reverse-coded negative attitude subscale, indicating lower negative attitudes toward AI. These findings suggest that students' attitudes toward AI are shaped not only by technological interest but also by perceptual factors related to AI. Therefore, integrating clinically oriented AI content and awareness-building activities into medical education may support the development of more balanced and informed attitudes toward AI.
Batuhan Horasan, A. Ergin, Eda Şenarabacı· BMC Medical Education· 0 citations
Background: Artificial intelligence (AI) is increasingly being applied in medicine for diagnosis, treatment, and decision-making. While enthusiasm for AI training among healthcare workers has been reported globally, little is known about awareness and attitudes in Afghanistan, where limited access to advanced diagnostic tools makes AI particularly valuable. Objective: This study aimed to assess the knowledge, attitude, and practices of AI among healthcare workers in Kabul City. Methods: A cross-sectional survey was conducted from January to February 2025 among 256 healthcare staff, including physicians, nurses, technicians, and administrative personnel. Data were collected using a structured questionnaire distributed online and in paper format. Responses were recorded on a three-point Likert scale. Statistical analysis was performed using SPSS version 26, employing descriptive statistics, chi-square tests, regression, and correlation analyses. Reliability was assessed using Cronbach’s alpha. Results: Of the participants, 71.1% were male (28.9% were female), and 43.8% were aged 21–29 years. Knowledge of AI was limited: only 1.6% demonstrated good knowledge, 44.7% poor knowledge, and 53.9% had insufficient knowledge. Attitudes were more favorable, with 47.3% expressing positive views, 37.5% somewhat agreeing, and 15.2% expressing negative views. Regression analysis revealed that age was significantly associated with knowledge scores, and knowledge strongly predicted positive attitudes. Reliability analysis confirmed acceptable internal consistency across domains (α ≥ 0.72). Conclusion: Knowledge of AI among medical staff in Kabul is limited, but attitudes are generally favorable. Structured training programs, conferences, and AI-enabled systems are needed to strengthen Afghanistan’s healthcare sector.
Ahmad Mustafa Rahimi, Abdul Bashir Bashari, M. Mohammadi et al.· Annals of Medicine and Surge...· 0 citations
The normalisation of АІ in academic practice indicates its role as a cognitive extension in medical education, which necessitates the development of structured educational strategies and methodological guidelines for its responsible use in medical training.
Inna I. Kucherenko, A. O. Burdeinyi, L. Lymar et al.· Polski merkuriusz lekarski :...· 0 citations