Aug 2026· Work· pp.
10519815261477732
· 0 citations· 35 references
Medicine
TL;DR
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.
Abstract
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.
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
Background: Artificial intelligence (AI) is increasingly being used in medical education, offering opportunities for personalized learning, rapid access to information, and support for academic activities. However, concerns regarding accuracy, overdependence, and reduced critical thinking remain.
Objectives: To assess the perceptions, attitudes, perceived benefits and concerns regarding the use of AI tools among undergraduate medical students.
Methods: A cross-sectional study was conducted among 767 undergraduate medical students at a tertiary care teaching institution in Bharuch, Gujarat, India, from January to March 2026. Students from all MBBS years who provided informed consent were included. Data were collected using a pre-tested, semi-structured questionnaire administered through Google Forms. Descriptive statistics were used to summarize the findings as frequencies and percentages.
Results: Among 767 participants, AI use was reported daily by 207 (27.0%) students, on a few days per week by 282 (36.8%), and occasionally or rarely by 278 (36.2%). ChatGPT was the most commonly used AI tool reported by 698 (91.0%) students, followed by Google Gemini 398 (51.9%). The most common academic purpose of AI use was clarifying difficult concepts, reported by 561 (73.1%) students, followed by obtaining quick summaries or notes 460 (60.0%). AI was considered very helpful for understanding concepts by 445 (58.0%) students, while 515 (67.1%) reported faster learning. Nearly half 367 (47.8%) supported integrating AI into medical teaching. However, inaccurate information 334 (43.4%), reduced critical thinking 327 (42.6%), and overdependence 252 (32.8%) were major concerns.
Conclusions: AI tools are widely used and positively perceived by medical students. Structured and responsible integration of AI into medical education may maximize its benefits while minimizing potential risks.
Keywords: Artificial Intelligence, ChatGPT, Medical Education, Medical Students
Vaishali Patel, Vallari Jadav, Kuntal Patel· International journal of sci...· 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.
Digital literacy was the strongest independent factor associated with attitudes towards artificial intelligence and perceptions regarding the impact of artificial intelligence on clinical reasoning, and perceptions regarding the impact of artificial intelligence on clinical reasoning were also independently associated with attitude scores.
Nur Demirbaş, Hatice Kucukceran, O. Arık· Postgraduate medical journal· 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
Background: Artificial intelligence (AI), often termed the "fourth industrial revolution," is transforming healthcare by improving decision-making, diagnosis, and patient management. Despite its potential, understanding and integration of AI into medical education and practice remain limited, especially in resource-constrained regions. This study explores medical students' knowledge, attitudes, and practices concerning AI in a rural medical college in Haryana, India. Material and Methods: A cross-sectional online survey was conducted among 189 medical students at BhagatPhool Singh Government Medical College for Women, Haryana. Data were collected using a 13-item questionnaire on demographic details, knowledge, attitudes, and practices related to AI. Convenience sampling was employed, and data analysis was performed using SPSS v26.0. Frequency tables were used to summarize the findings. Results: Among respondents, 91.5% were aware of AI, yet only 53.4% knew about its medical applications. Most participants (81.5%) reported not being taught about AI during medical school, but 79.9% expressed an interest in learning more. While 76.7% agreed AI aids in early diagnosis, only 20.6% believed it could replace doctors. Regarding AI's potential burden, 18.5% agreed, while 45.5% remained neutral. For future practices, 51.9% showed willingness to work with AI, 66.7% believed it could aid in information gathering, and 78.8% thought AI would revolutionize medical teaching and research. Conclusion: The majority of medical students demonstrated a positive attitude toward AI and recognized its potential in healthcare, despite limited knowledge of its applications. Specific training and curriculum integration of AI in medical education are essential to prepare future doctors for the evolving digital landscape. Keywords: Artificial intelligence, medical education, medical students, rural healthcare, AI applications.
