Awareness and Attitudes of Medical Sciences Students at Shahid Sadoughi University of Medical Sciences toward Artificial Intelligence: Challenges and Opportunities in Modern Medical Education
Jul 2026· Journal of Medical Education Development· 0 citations
TL;DR
Although students showed positive attitudes toward AI, their conceptual and practical awareness was moderate, and integrating structured educational programs focusing on practical and ethical aspects of AI into the medical curriculum is essential.
Abstract
Introduction: Rapid advancements in artificial intelligence (AI) have brought significant transformations to healthcare systems and medical education. Medical sciences students' awareness and attitudes toward AI play a crucial role in its acceptance and effective use. This study aimed to assess the level of AI awareness and attitudes among students at Shahid Sadoughi University of Medical Sciences, Yazd, Iran, and examine related demographic factors.
Method: This descriptive cross-sectional study was conducted in 2024 with 500 undergraduate and general medicine (MD) students from multiple faculties. Data were collected using a researcher-developed and standardized questionnaire covering demographics, AI-related awareness, attitudes, and concerns. Content validity was confirmed by 10 experts (CVI = 0.89), and reliability was verified (Cronbach's alpha = 0.83). Data analysis was performed with chi-square test, independent samples t-test, and ANOVA.
Results: Among participants, 62% were female and 90% were under 25 years old. The highest awareness and most positive attitudes were found among medical and midwifery students, while emergency medical services and operating room students showed the lowest levels (P < 0.05). Mean awareness and attitude scores were significantly higher in females than males (P < 0.05), with no significant differences across age groups. Concerns about job displacement due to AI were also prominent.
Conclusion: Although students showed positive attitudes toward AI, their conceptual and practical awareness was moderate. Integrating structured educational programs focusing on practical and ethical aspects of AI into the medical curriculum is essential.
Healthcare students had unsatisfactory awareness of AI, and their overall attitude was negative, highlighting an urgent need for an integrated AI curriculum tailored to their actual needs.
Waghachavare Vivek B., Dhobale Randhir V., Gore Alka D. et al.· International journal of com...· 0 citations
Undergraduate medical students show moderate awareness of AI in healthcare but lack formal training and in-depth understanding, highlighting the need for structured AI education within medical curricula and further research on its long-term impact.
Sonali Sharma, Smriti Kayat, N. Saboo et al.· Nigerian Medical Journal· 0 citations
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
BackgroundArtificial intelligence (AI) is increasingly integrated into healthcare and health-professions education. While it may improve efficiency, decision support, and learning, it also raises concerns about workforce impacts, safety, privacy, and accountability. Gulf-region evidence on how students and faculty perceive AI across disciplines within a single health science center remains limited.ObjectiveTo assess attitudes toward AI among health-sciences students and faculty and examine demographic factors associated with these attitudes.MethodsA descriptive cross-sectional online survey using convenience sampling was distributed to Health Science Centre (HSC) students and faculty (605 students, 43 faculty). It included demographic variables and two standardized measures, the General Attitudes towards Artificial Intelligence Scale (GAAIS) and the Artificial Intelligence Attitude Scale-4 (AIAS-4). Internal consistency was assessed using Cronbach's alpha; groups were compared using Mann-Whitney U and Kruskal-Wallis tests.ResultsAttitudes were broadly positive in both groups. Faculty scored higher than students on GAAIS (62.0 vs 55.5; p < 0.001) but not AIAS-4 (68.7 vs 63.1; p = 0.094). Among students, males scored higher on GAAIS (61.2 vs 54.9; p < 0.001) and AIAS-4 (71.4 vs 62.2; p = 0.004). AIAS-4 also differed by nationality (Kuwaiti 64.3 vs non-Kuwaiti 56.6; p = 0.005) and major (p < 0.001), with lower-GPA students less positive. In multivariable regression, male gender, Kuwaiti nationality, higher GPA, and discipline were independently associated with AIAS-4; physical-therapy students scored significantly lower than several disciplines.ConclusionsStudents and faculty demonstrate broadly positive attitudes toward AI alongside concerns about safety, surveillance, and workforce displacement. Structured 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
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 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