Aspects of the use of artificial intelligence in medical education: Results of a cross-sectional survey conducted at Bogomolets national medical university.
Jul 2026· Polski merkuriusz lekarski : organ Polskiego Towarzystwa Lekarskiego· Vol 54 3, pp.
300-305
· 0 citations· 11 references
Medicine
TL;DR
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.
Abstract
Objective
Aim: To analyze the key aspects of artificial intelligence (AI) application in medical education and to assess related psychological attitudes among students and academic staff.
PATIENTS AND
Methods
Materials and Methods: A cross-sectional study was conducted using an anonymous online survey. A total of 727 respondents participated, the majority of whom were involved in medical education. The questionnaire included demographic items, questions on frequency and purposes of AI use, self-assessment of AI proficiency, and a 40-item scale evaluating psychological attitudes. Data were analyzed using descriptive statistics and presented as relative frequencies (%).
Results
Results: The findings demonstrated significant integration of АІ into the medical education environment: 41.4% of respondents reported daily use, while 46.6% reported using these tools several times per week. Primary applications included learning support (79.6%), self-education (78%), and preparation of educational materials (30.1%). While 61.8% of participants rated their proficiency as high, the study raised concerns about the rapid pace of technological development and potential cognitive dependence.
Conclusion
Conclusions: The normalisation of АІ in academic practice indicates its role as a cognitive extension. While it enhances efficiency and creativity, the risk of reduced autonomy and ethical challenges necessitates the development of structured educational strategies and methodological guidelines for its responsible use in medical training.
AbstractObjective: The integration of artificial intelligence (AI) into medical education is rapidly increasing; however, gaps remain in terms of learners’ knowledge and ethical awareness. This study aimed to evaluate baseline perceptions of AI among medical students and faculty members and to assess the impact of a structured educational intervention on students’ knowledge, attitudes, and ethical awareness.Method: This study employed a single-group pretest–posttest design. Initially, a 24-item questionnaire was developed on the basis of a comprehensive literature review and administered to medical students (n = 123) and faculty members (n = 17) to assess baseline perceptions. Following an expert review involving medical education specialists from three universities, the instrument was refined to a 12-item artificial intelligence awareness questionnaire. The finalized questionnaire was administered as both a pretest and posttest to 143 medical students. After the educational intervention, 123 students completed both assessments and were included in the paired analyses. Data were analyzed using paired samples t tests, and effect sizes were calculated using Cohen’s d.Results: At baseline, both students (mean = 3.55 ± 0.58) and faculty members (mean = 3.66 ± 0.53) demonstrated moderate levels of AI awareness. Knowledge of fundamental AI concepts was relatively limited, while attitudes toward the future importance and educational utility of AI were highly positive. Following the intervention, statistically significant improvements were observed across all knowledge and awareness domains (P < .001). The greatest gains were observed in understanding AI concepts, applications in medical education, and ethical considerations. The overall questionnaire score significantly increased (mean difference = -1.01, t(122) = -11.745, P < .001), indicating a strong positive effect of the intervention. However, the participants’ perceived need for further AI training remained high and did not significantly change (P = .060).Conclusion: Although baseline knowledge of AI among medical students and faculty members was limited, both groups demonstrated strong positive attitudes and a clear demand for further training. The educational intervention significantly improved AI-related knowledge and ethical awareness. These findings highlight the importance of integrating structured AI education into medical curricula to support the responsible and effective use of emerging technologies.
Ay Sıla Çaloğlu, Halid Durna, Zeynep Naz Ergen et al.· Journal of Medical Education...· 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
OBJECTIVE
Aim: To analyze key aspects of the application of AI as a clinical mentor in telemedicine-based medical education, and to assess the related opportunities, challenges, and attitudes of pediatric students.
PATIENTS AND METHODS
Materials and Methods: A cross-sectional study was conducted to assess the level of digital competence of future pediatricians regarding telemedicine and AI. A total of 251 students from the Faculty of Pediatrics at Bogomolets National Medical University (BNMU) participated in the survey. Participants were aged 17 to 26 years, of whom 17.9% were male and 82.1% were female. The questionnaire included items on demographic characteristics, knowledge and use of telemedicine and AI tools, self-assessment of digital competence, and attitudes toward the integration of AI in healthcare. Data were analyzed using descriptive statistics and presented as relative frequencies (%).
RESULTS
Results: The study assessed digital competence and the use of AI among 251 future pediatricians regarding telemedicine-based medical education. Survey results indicated active engagement with AI tools in educational and professional tasks (63.7% positive responses), moderate confidence in integrated digital devices (55.0%), and cautious self-assessment of clinical decision support systems (53.4%). These findings informed the development and revision of courses at BNMU, enhancing students' practical skills and digital competence.
CONCLUSION
Conclusions: Future pediatricians actively engage with digital tools and AI, although confidence is higher in AI tasks than in integrated devices or clinical decision systems. The findings guided the development of updated courses to strengthen digital competence, practical skills, and critical evaluation of AI. Successful telemedicine implementation requires addressing data security, regulation, human-centred care, and digital inclusion to ensure safe, effective, and equitable healthcare.
Inna I. Kucherenko, Oxana Vygovska, Vadym G Terentyuk et al.· Wiadomosci lekarskie· 0 citations
AIM
This study aimed to evaluate the attitudes and perceptions of parents of pediatric patients in our sample towards the growing use of artificial intelligence in dental practices.
MATERIALS AND METHODS
The descriptive cross-sectional survey study was conducted with parents of pediatric patients who visited the Kırıkkale University School of Dentistry for routine dental examinations between 2024 and 2025. A 23-question survey was designed for participants to complete. The survey was evaluated as a scale consisting of three sub-dimensions. The data were analyzed using SPSS 27.
RESULTS
A total of 74.4% of participants reported having an average level of self-reported knowledge about the digital world (internet/technology). The biggest advantage of AI identified by the participants was saving time (64.3%). The AI-Health Education Score differed significantly according to educational status (p = 0.003). The participants with a bachelor's degree had higher mean scores on AI-Health Education than high school graduates. The effect size was small (η²=0.051). The mean Physician-AI Collaboration Score for men was significantly higher than that for women. The effect size was small (Cohen's d = -0.305).
CONCLUSION
These findings suggest that, within this sample, parents generally view AI positively and may be receptive to its integration into dental practice. These results may support the appropriate integration of AI into dental practice. It is also necessary to assess the effects on patient satisfaction and the quality of healthcare services.
Esra Hato, Büşra Çelik Kalaağası, M. Almaz et al.· BMC Oral Health· 0 citations
Objective:
To assess the attitudes of physicians, residents, and medical students toward artificial intelligence (AI) technologies, the prevalence and patterns of AI use in clinical practice and healthcare in Russia; and to characterize expectations and barriers to its implementation.
Material and methods.
A cross-sectional online survey was conducted from March 1 to July 15, 2026. The primary array consisted of 1169 returned questionnaires. After applying the exclusion criteria (age <18, technical data entry anomalies), 8 questionnaires were excluded. All subsequent calculations were performed on the final analytical sample of 1161 questionnaires. Statistical analysis involved Spearman's rank correlation, Kruskal–Wallis criterion, cluster analysis (k-means), principal component analysis (PCA).
Results.
The sample included 809 (69.7%) women, and 352 (30.3%) men, with the average age of 37.5±14.6 years (median 33 years, range 18–83 years). Among the respondents, 706 (60.8%) regularly or periodically use AI in medical activities, with the most common tool being large language model-based chatbots used by 620 (53.4%) participants. Thirty (2.6%) respondents fully trust the conclusions of AI systems, while 587 (50.6%) demonstrate insignificant trust. The perceived accuracy of AI diagnosis was 47.6% on average, while the perceived accuracy of diagnosis by physicians was 72.4% (a gap of 24.8%, stable in all age groups). Specialists <29 years old are more likely to use AI; the highest readiness was recorded in the group of 30–39 years. The use of AI outside of professional medical practice was the strongest predictor of its clinical use (ρ=0.65; p<0.001). Evaluation of diagnostic accuracy by physicians was not related to any indicator of AI acceptance (p<0.05). Cluster analysis (k=4) revealed four types of digital disposition: “Enthusiasts” (n=262), “Pragmatists” (n=310), “Observers” (n=269), and “Skeptics’ (n=320). The PCA confirmed a two-factor structure: acceptance of AI (PC1 39.4%) and trust in accuracy (PC2 15.0%).
Conclusion.
The gap between the declared acceptance of technology and actual behavioral engagement remains a persistent characteristic of the professional medical environment. A comparative analysis of age groups refutes the simplistic notion that “younger people are more supportive of AI, while older people more resistant to it”. Improving the overall digital literacy of medical professionals is a more effective and adaptable approach to reducing barriers to AI adoption than specialized medical AI training programs. At the same time, age predicts behavior rather than attitude: educational interventions aimed at changing attitudes towards technology are applicable to all age groups equally.
D. Korabelnikov, A. Lamotkin· FARMAKOEKONOMIKA. Modern Pha...· 0 citations