JMIR Publications

ABSTRACT

Background:

Introduction: 30 Artificial intelligence (AI) refers to modern systems that use self-learning algorithms to perform tasks 31 and generate responses that mimic human intelligence(1). Despite its early beginnings, the outburst in its popularity and success was in 2022 when OpenAI launched ChatGPT. Within just 5 days, the 33 platform gained one million users, making it the second-fastest application in history to reach this 34 milestone(2,3). 35 In the medical domain, AI has proved itself as a valuable tool for both education and healthcare. For 36 students, AI assists in editing texts, generating medical case scenarios, summarizing complex topics, 37 and simplifying difficult information, which save time and enhance efficiency(4,5). In healthcare, AI 38 has proved itself as a valuable tool for medical practitioners. It has passed the USMLE exams and 39 proved its efficiency in disease diagnosis across multiple specialties. Its performance is particularly 40 notable in specialties that relies on image analysis such as radiology and pathology(6–10). However, 41 these advancements are not without doubts and concerns. The rapid growth of AI has sparked 42 debates over ethical issues, job displacement in healthcare, and other challenges(11,12). 43 The integration of AI tools in medical education is crucial, as this new and fast-growing technology 44 will shape the future if healthcare, despite its absence from traditional medical curricula. The World 45 Medical Association has rendorsed AI integration into medical education; however, its 46 implementation is still not sufficient worldwide(13,14). This gap presents a notable challenge in low- 47 resource areas, where limited access to computers, reliable electricity, and internet connectivity 48 further hinders adoption(15). 49 Syria has endured a devastating war since 2011, resulting in millions of refugees both inside and 50 outside the country(16). The conflict has huge impacts on all aspects of life, including healthcare and 51 medical education. Over half of medical facilities were destroyed or damaged during the war(17–19). 52 Moreover, the concurrent economic collapse and infrastructure devastation have created severe 53 poverty and shortages in basic necessities, including food and electricity among others(20). These 54 factors have significantly impacted medical education and access to knowledge. One notable 55 consequence is that most medical students now intend to immigrate after graduation, with Germany 56 emerging as the primary destination for Syrian doctors(21–23). 57 The study aims to evaluate Syrian medical students' experiences with artificial intelligence tools and 58 their perceptions regarding AI's role in medical education and providing healthcare. We aim to 59 examine how war-related consequences -including limited computer access due to economic 60 constraints and high emigration intentions- influence the experiences and perceptions of AI. 61 Additionally, we aim to explore the differences across academic years to assess whether experiences 62 and perceptions are evolving positively over time. These findings will provide insights into the 63 readiness of Syria's future doctors to utilize AI tools in the future, despite having trained in an 64 unsupportive environment for modern technologies.

Objective:

This study highlights the increasing reliance on AI tools among medical students and graduates for academic and clinical purposes. The highest usage was reported in study preparation, writing tasks, and clinical simulations. Significant differences in AI usage were observed based on academic level, gender, access to technology, and research experience. While perceptions were largely positive concerns remained around ethical use, potential job displacement, and diminished human interaction in medicine. These findings underscore the importance of developing institutional policies to guide the ethical and effective integration of AI in medical education. Given these outcomes, we believe that this manuscript is suitable for publication in JMIR MEDICAL EDUCATION

Methods:

Methods:

66 Study Design and Setting: 67 This study is a cross-sectional, descriptive survey conducted in April and May 2025 among medical 68 students in Syria. The primary aim was to explore the experiences of medical students and recent 69 graduates with artificial intelligence (AI) tools, as well as their perceptions of AI’s role in medical 70 education and clinical practice. 71 Participants and Sampling: 72 A total of 400 participants were enrolled in the study, with 100 samples collected per academic year 73 over four consecutive years. The sample included clinical-year medical students (fourth and fifth year 74 of medicine) and graduate and prospective graduate students. Participants were recruited through 75 convenience sampling via student networks and social media platforms. 76 Exclusion criteria included pre-clinical students (first to third year) from faculties of human medicine, 77 as well as postgraduate specialty trainees (residents). 78 Inclusion criteria included current enrollment in clinical medical training or graduation within the 79 past two years, as well as willingness to voluntarily complete the questionnaire. Participation was 80 anonymous and uncompensated. 81 Data Collection Instrument: 82 Data were collected using a structured, self-administered online questionnaire. The original 83 questionnaire was developed in English and professionally translated into Arabic by experts in 84 medical education and linguistics. 85 The choice of question formats and content was guided by researchers’ observations of the Syrian 86 community context and the socioeconomic challenges resulting from the ongoing Syrian crisis. 87 The final Arabic version of the questionnaire was reviewed and validated by domain experts prior to 88 distribution. It consisted of four main sections: 89 1. Demographic and academic information – including age, gender, academic year, computer 90 ownership, language learning background, and prior research experience. 91 2. Experience with AI tools – assessing the use of tools like ChatGPT and digital anatomy platforms 92 across multiple contexts such as studying, exam preparation, clinical decision-making, and research 93 writing. 3. Perceptions of AI – evaluated using a 5-point Likert scale (from strongly disagree to strongly 95 agree), addressing AI’s role in learning, medical ethics, patient care, and its future impact on the field 96 of medicine. 97 4. Factors influencing AI adoption – including clinical exposure, language learning goals (e.g., 98 German), emigration intentions, and access to digital technology. The survey incorporated items 99 derived from prior literature and was pilot-tested with a small sample of medical students to ensure 100 clarity and contextual relevance. 101 Data Analysis: 102 Quantitative data were analyzed using IBM SPSS Statistics (version 27 ). Descriptive statistics 103 (frequencies, means, percentages) were used to summarize participant characteristics and AI usage 104 behaviors. Inferential analyses included: 105 Chi-square tests to assess associations between categorical variables (e.g., gender, AI usage 106 patterns). Mann–Whitney U tests to compare differences in AI perceptions between subgroups. 107 Binary logistic regression models to determine predictors of positive perceptions and AI-related 108 behaviors. Predictor variables included gender, clinical status, prior AI experience, and research 109 background. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were reported. A p-value 110 < 0.05 was considered statistically significant. 111 Ethical Considerations: 112 Ethical approval for biomedical research was obtained from the Ethical Approval for Biomedical 113 Researchers (EABR) committee under approval number 1612. All participants provided informed 114 digital consent before participating. Responses were collected anonymously, and all data were stored 115 securely to ensure confidentiality and privacy.

Results:

Results:

117 Demographic characteristic: 118 A total of 400 medical students and recent graduates participated in the study. The sample was 119 nearly gender-balanced (51.5% male, 48.5% female), with most participants aged between 20 and 24 120 years (84%). Half of the respondents were clinical-year students, and the other half were either 121 graduated or prospective graduates. A majority (61.3%) resided in urban areas. Regarding income, 122 43% reported a good income level, while 38% reported moderate income. Notably, 72.5% of 123 participants expressed a desire to pursue postgraduate specialization abroad. In terms of technology 124 and language exposure, 68.8% reported having access to a personal computer. 39.8% had studied 125 the German language, with 51% of those using AI tools to support their learning. 28.2% had prior research experience. In English proficiency, 59.3% rated themselves as good and 35.5% as excellent. 127 Knowledge about AI came primarily from social media (73.5%), followed by reading research (15%), 128 and discussions with IT-savvy friends (9.75%). Prior experience in Using AI: 132 Academic and clinical tasks for which Chatgpt was used with notable proportions as follows: 57% 133 used AI to assist with studying or exam preparation, 40% used it to complete written assignments, 134 35.5% used it to suggest research topics or questions, 33.8% used it to write research papers, 23.3% 135 used it to help with writing case reports, 21.8% used it to generate self-assessment questions, 11.8% 136 used it to help write patient notes Using AI technologies during medical school was also reported where 72.3% participants used digital 138 anatomy tools, 42.8% used computational pathology tools, 22.5% used AI-generated cases for clinical 139 simulation. Using AI during residency: 148 In a more comprehensive set of questions, participants reported using AI for broader educational and 149 clinical purposes during residency: 85.5% showed interest for using AI to help answer medical 150 questions, 82.8% to explore new medical topics or research, 80.8% to assist with studying or 151 preparation, 75% to help write research papers, 65.3% to help write case reports, 52.8% to help write 152 patient notes, 52% to assist in clinical decision-making. Perceptions of artificial intelligence (AI) for career, education and patient care: 156 Descriptive analysis of participant responses revealed generally favorable perceptions of artificial 157 intelligence (AI) in medical education and practice. Items were rated using a five-point Likert scale, 158 and positive perceptions were defined as the combined percentage of “Agree” and “Strongly Agree” 159 responses. 160 1. AI in Learning and Career Development: 161 76% of participants agreed that ChatGPT could improve their learning during residency, 47.3% 162 agreed that AI tools effectively met their needs during medical school, 48% preferred using ChatGPT 163 over traditional resources such as Google or medical references, 70.8% agreed that the answers 164 provided by ChatGPT need to be verified, 75.3% reported looking forward to using more advanced 165 versions of ChatGPT or AI in their future careers, just 28.8% reported that their peers have always 166 used ChatGPT ethically, 71.8% supported the implementation of institutional policies regulating AI 167 use by trainees, just 25.5% were agree that AI would create more career opportunities, 11% stated 168 that AI had influenced their specialty choice, 33.3% agreed with the statement that AI would limit job 169 opportunities. 170 2. AI in Patient Care: 171 54.6% expressed concern about AI’s ethical impact on healthcare, yet 42.1% agreed that AI would 172 improve diagnostic accuracy, 58.8% believed AI would enhance patient care, 64.3% expected AI to 173 significantly impact the healthcare system overall, 57.6% were concerned that AI might reduce the 174 humanistic aspect of medicine, 43.1% agreed that AI would help reduce medical errors, 62.5% 175 expressed concern that AI might reduce patient trust in physicians. 176 Although some concerns were evident—particularly regarding ethics and the humanistic dimension 177 of care—overall, a majority of students expressed optimism toward the integration of AI in both 178 educational and clinical domains. Prior experience in using Ai between students’ demographic characteristics: 184 A series of chi-square tests were conducted to assess associations between students’ demographic 185 characteristics (academic level, gender, prior research experience, computer availability, and German 186 language exposure) and their use of AI technologies (Chatgpt) in various educational contexts. Significant associations were identified between academic level and multiple AI use cases. Clinical 188 year students were more likely than graduate and prospective graduate students to use AI for 189 studying or exam preparation (p < .001), generating self-assessment questions (p = .021), suggesting 190 research topics (p < .001), writing research papers (p = .015), completing written assignments (p < 191 .001), creating simulated clinical cases (p = .031) and help write patient notes (p = .02). 192 Gender was significantly associated with the use of AI for writing research papers (p = .004), suggest 193 research topics or questions (p = 0.039), using digital anatomy (p = 0.023) where male students 194 reported higher usage. 195 Students with prior research experience were significantly more likely to use AI for suggesting 196 research topics (p = .039), writing research papers (p < .001), writing case reports (p = .002), and 197 using digital anatomy platforms (p < .001). 198 Having access to a personal computer was also positively associated with using AI to help write 199 research papers (p = .005) and to support learning in digital anatomy platforms (p = .001). 200 Although Learning German language was not statistically associated with most AI uses, students who 201 are studying German reported slightly higher use of AI for language learning as to help complete 202 written assignments (p= .017) and they used AI to help write research papers more than students 203 who aren’t studying German (p = .004). This trend reflects a culturally motivated behavior, 204 particularly among Syrian students, who often aspire to pursue postgraduate training in Germany 205 due to the ongoing crisis. These students appeared to use AI to simulate German language 206 acquisition, highlighting AI's potential role in educational mobility and international preparation. Perceptions of artificial intelligence (AI) for career and education between students’ demographic 209 characteristics: 210 A series of Mann–Whitney U tests were conducted to compare perceptions of AI for career and 211 education among medical students based on academic level, gender, prior research experience, 212 access to a personal computer, and German language exposure. 213 Graduate and prospective graduate students expressed significantly more positive perceptions 214 regarding ChatGPT’s usefulness in learning during residency (p = 0.026), suggesting AI is seen as a 215 valuable educational aid during clinical rotations, and they believed that Medical schools and residency programs should develop policies about the use of Chatgpt and AI by trainees more than 217 clinical years' students (p = 0.017). 218 Whereas AI was effective in meeting Clinical years' students' needs during medical school more than 219 graduate - prospective graduate students (p = 0.001). 220 Male students rated AI significantly higher in Supporting residency learning (p = 0.007), Preference 221 for ChatGPT over other search engines (p = 0.003), Enthusiasm toward future versions of AI (p = 222 0.009). 223 AI’s potential has impacted residency specialty choice of students with no personal computers more 224 (p = 0.036), whereas students with personal computers believed that their peers have used chatgot 225 ethically less than who don’t have (p = 0.007). 226 Students who had learned German rated ChatGPT as more helpful for learning during residency (p = 227 0.005), which may reflect AI’s perceived value in language acquisition for migration-preparing 228 students. Also, they showed enthusiasm toward future versions of AI (p = 0.003). 229 Those with prior research experience were less confident in ChatGPT’s accuracy (p < 0.001), more 230 supportive of medical schools establishing policies for AI use (p = 0.005), have more positive 231 perceptions regarding ChatGPT’s usefulness in learning during residency (p = 0.034). whereas those 232 with no experience perceived their peers as using ChatGPT more ethically (p = 0.014), and their 233 residency choice has been impacted more by AI’s potential (p = 0.038). Perceptions of artificial intelligence (AI) for patient care between students’ demographic 242 characteristics: 243 A series of Mann–Whitney U tests were conducted to compare perceptions of AI for patient care and 244 Professional Practice among medical students based on academic level, gender, prior research 245 experience, access to a personal computer, and German language exposure. 246 Gender again played a role where Male students had significantly higher agreement that AI will 247 improve diagnostic accuracy (p < 0.001), improve patient care (p < 0.001), AI will have a major impact 248 on healthcare (p < 0.001), reduce medical errors (p = 0.005), whereas female students worry more 249 about the ethical impact of AI on healthcare (p = 0.019). 250 Graduate and prospective graduate students believed more that AI will have a major impact on 251 healthcare during residency (p = 0.033). 252 German language learners showed significantly higher agreement with the statement that AI will 253 improve patient care (p = 0.007), have a major impact on healthcare (p = 0.016) and will enable them 254 to make more accurate diagnoses (p = 0.001). 255 Prior research experience was associated with more concern that AI will reduce patient trust in 256 physicians (p = 0.028).Across separate binary logistic regression models predicting AI perceptions, two factors consistently 259 emerged as key drivers. Prior AI usage experience significantly increased the odds of a favorable 260 perception in multiple domains: each additional AI‐use task raised the likelihood that students would 261 agree ChatGPT improves learning during residency (adjusted OR=1.43, 95% CI 1.15–1.79, p=.002), 262 that it was effective in meeting medical‐school needs (aOR=1.60, 95% CI 1.34–1.92, p<.001), and that 263 it would help reduce medical errors (aOR=1.14, 95% CI 1.01–1.28, p=.040). 264 Clinical‐year status was consistently associated with more reserved views: clinical year students were 265 less likely to believe ChatGPT enhances learning during residency (aOR=0.26, 95% CI 0.11–0.64, 266 p=.003), less likely to anticipate using future versions (aOR=0.18, 95% CI 0.07–0.49, p<.001), and less 267 likely to expect AI to have a major impact on healthcare (aOR=0.24, 95% CI 0.09–0.65, p=.005). 268 Gender differences appeared: female students had three times the odds of insisting on verifying 269 ChatGPT’s answers (aOR=3.12, 95% CI 1.36–7.15, p=.007) and female students were twice as likely to 270 worry about AI’s ethical impact on healthcare (aOR=2.06, 95% CI 1.19–3.54, p=.010). 271 Prior research experience did not significantly predict any positive perception once other factors 272 were controlled.

Conclusions:

Conclusion: 460 This study reveals a predominantly optimistic outlook among Syrian medical students and residents 461 regarding the role of AI in education and clinical care. High levels of prior usage suggest both 462 accessibility and growing technological literacy, though notable concerns—particularly ethical and 463 relational—underscore the need for guided integration. Differences across gender, training stage, 464 and socioeconomic proxies reflect the nuanced landscape of AI perception in medical education. 465 While enthusiasm is widespread, cautious appraisal of AI’s limitations and its potential to disrupt 466 traditional human-centered care remains essential. As such, integrating AI into medical education 467 must be done strategically, emphasizing critical appraisal, ethical awareness, and reinforcement of 468 compassionate clinical reasoning. These findings contribute meaningfully to the regional and global 469 discourse on responsible AI adoption in health professions education.