Embracing Artificial Intelligence: Evaluating Technological Adaptability in Palestinian Medical Education

Embracing Artificial Intelligence: Evaluating Technological Adaptability in Palestinian Medical Education | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Embracing Artificial Intelligence: Evaluating Technological Adaptability in Palestinian Medical Education Hamdah Hanifa, Alhareth M. Amro, Salahaldeen Deeb, Ammir Abuzahra, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6623487/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Artificial intelligence (AI) enables computers to process data and solve problems via algorithms, with China at the forefront of medical AI applications like diagnostics. Medical students increasingly rely on AI tools (e.g., ChatGPT) for education, and ML advances predictive research. Methods A cross-sectional study assessed AI readiness among 799 medical students from all universities in the West Bank, Palestine, that have a Faculty of Medicine, using the validated MAIRS-MS scale (22 items across 4 domains). Data collection combined electronic and paper questionnaires, ensuring high participation and reliability (α = 0.87). Results Most participants were from Hebron University (66%) and represented all academic years. The majority (83%) were aware of AI in medicine, and 73% had prior experience with AI tools. The median total readiness score was 73 (IQR: 66–84), with highest scores in ability (median: 27) and cognition (median: 26), and lower scores in vision and ethics (both median: 10). Males, older students, high-GPA achievers, and those from higher-income backgrounds had significantly higher readiness scores (p < 0.001). Prior AI experience and awareness were also strongly associated with increased readiness. No significant differences were observed across universities or academic years. Conclusion Palestinian medical students demonstrate moderate to high readiness for AI integration in medicine, particularly in technical and cognitive domains. However, notable gaps remain in ethical understanding and visionary thinking. Addressing these gaps requires national curricular reform focused on ethics, regulation, and strategic AI applications. Equitable access to AI education across socioeconomic and gender lines is essential to prepare future physicians for a digitally enhanced healthcare landscape. Artificial intelligence MAIRS_MS Cross sectional Chat GPT Medical students Palestine Background Artificial intelligence (AI) is scientifically defined as the ability of computing systems to understand and leverage given algorithms to analyze data to achieve specific goals, as highlighted by Kaplan and Haenlein. China is one of the leading countries in adopting an advanced approach based on AI and its various tools in the medical field. Its applications include medical data analysis, interpretation of radiographic images, and achieving diagnosis and treatment 1 . Even at the university level, the digital revolution of AI has become evident among students, especially medical students who have relied on applications like DeepSeek, Chat GPT, and Copilot to understand medical materials, translate texts, solve scientific questions, and explain the complex mechanisms of diseases 2 . Recent statistics indicate that the number of Chat GPT users has exceeded 100 million in just two months, reflecting its wide-ranging reliance in many fields 3 . Furthermore, machine learning (ML) has gained widespread acceptance among medical researchers due to its advanced capabilities, such as predicting five-year survival rates in cancer patients 4567 . The multiple benefits of AI require healthcare workers and students to develop skills in using this technology in their specialties in a way that serves patients and improves medical quality 8 . However, companies like Epic, Amazon, and Google have expressed objections to some of the proposed regulations from the United States to regulate the use of AI in healthcare technologies 9 . This study aims to assess the level of awareness and engagement with AI technologies among Palestinian medical students enrolled in universities in the West Bank, Palestine. It is considered the first study of its kind to delve into this field, providing a comprehensive overview of the potential application of AI amid the challenges of wars and conflicts in the region. Methods Study Design and Participants: A cross-sectional study was conducted to assess the readiness of medical students to utilize artificial intelligence technologies in the medical field. The study included 799 medical students from all universities in the West Bank, Palestine, that have a Faculty of Medicine, specifically: Al-Quds University, An-Najah National University, Hebron University, Palestine Polytechnic University, and Arab American University. Involving students from all universities aimed to enhance the possibility of generalizing the results within a single national context, while ensuring a more homogeneous assessment of artificial intelligence readiness. The participants represented all stages of medical education, from basic sciences to clinical years. This comprehensive sampling strategy facilitated a detailed analysis of artificial intelligence readiness across different academic levels, providing valuable insights into the advanced competencies of future healthcare professionals. Data Collection Procedures: The study adopted a flexible approach combining electronic and paper questionnaires to ensure maximum participation. Questionnaires were distributed through official channels of medical colleges, as well as communication platforms such as WhatsApp groups and academic channels on Telegram. To enhance response rates, questionnaires were also distributed manually after lectures in classrooms, with quiet locations like libraries or university cafes provided to allow students to respond with focus. The questionnaire used in this study was adapted from the work by Karaca, O et al. (2021) titled " Medical artificial intelligence readiness scale for medical students (MAIRS-MS) – development, validity and reliability study" 10 . Prior to commencement, all participants underwent a brief instructional session (3–5 minutes) where the study's objectives and significance were explained, ensuring complete data confidentiality. The research team emphasized direct interaction with students to address any inquiries, enhancing trust and active participation. Exclusion Criteria : Students not enrolled in the relevant human medicine college under study. Participants who declined consent or withdrew before completing the questionnaire. Partial or incomplete responses (less than 90% of the questions). Data collection spanned from February 2 to April 20, 2025, to ensure comprehensive coverage across classes and examination periods. The average questionnaire response time was 8 minutes, with clear written and auditory instructions provided (through audio links for the electronic version) to simplify the process. Authors were assigned in each university to oversee the distribution and responses, with any qualitative research notes recorded. Ethical Considerations: The study adhered to the highest standards of academic integrity and ethical protection for participants. All data underwent a three-stage review process: Automated screening using outlier detection software. Manual review by the primary research team. 20% random review by an independent supervisor. Ethical approval was obtained from the Institutional Review Board (IRB) at Al-Quds University. Questionnaire: The study adopted the validated scientific tool "Medical Artificial Intelligence Readiness Scale for Medical Students" (MAIRS-MS), a multidimensional diagnostic tool consisting of 22 items distributed across four main axes: Ethical Axis (3 items): Assessing awareness of ethical challenges. Visionary Axis (3 items): Measuring the inclination towards technology adoption. Ability Axis (8 items): Evaluating practical skills. Cognitive Understanding Axis (8 items): Assessing theoretical understanding. It utilized a 5-point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree). Total Score Range: 22–110 points (higher scores indicate higher readiness). The scale demonstrated high reliability (α = 0.87) in previous studies 10 . The tool underwent rigorous adaptation processes including double translation (Arabic-English-Arabic), expert review, and a pilot study on a small sample (n = 30). Questionnaire Structure : 1) Introductory Section : Demographic data (gender, age, academic year…). Academic indicators (cumulative GPA). Previous experience with artificial intelligence. 2) Main Section : MAIRS-MS questions designed in a hierarchical sequence. This tool was specifically developed to measure technical readiness in medical educational settings, taking into consideration the cultural characteristics of the target sample, making it a comprehensive and accurate assessment tool. Statistical Analysis: Data were analyzed using SPSS Inc software, version 22. Descriptive statistics, including frequencies and percentages, were employed to summarize the participants' demographic characteristics. To examine differences in AI readiness levels between groups (e.g., participants with and without prior AI experience), independent samples *t*-tests were conducted. Assumptions of normality and homogeneity of variance were verified prior to analysis. Result A total of 799 medical students from five Palestinian universities participated in this cross-sectional study. The majority were from Hebron University (66%), followed by Al-Quds University (16%), Palestine Polytechnic University (8.9%), An-Najah National University (5.1%), and Arab American University (3.9%). The gender distribution was relatively balanced, with 53% identifying as male and 47% as female. Participants were drawn from all six academic years, with the largest representation from the fourth year (33%). Students ranged in age from 18 to 24 years or older, with 22-year-olds comprising the largest age group (27%). Most students reported a GPA between 3.0 and 3.5 (55%), with 14% exceeding 3.5, and 18% reporting a GPA between 2.5 and 3.0. Additionally, 83% of the students indicated awareness of artificial intelligence (AI) applications in medicine, and 73% had prior experience using AI-related tools or platforms in a medical context. Regarding socioeconomic status, the majority of participants reported a moderate-income level (67%), followed by high income (19%) and low income (14%) (Table 1 ). Table 1 Demographic Characteristics of Participants (N = 799) Category Subcategory N (%) Gender Male 426 (53%) Female 373 (47%) Age 18 73 (9.1%) 19 157 (20%) 20 124 (16%) 21 109 (14%) 22 216 (27%) 23 95 (12%) 24 or more 25 (3.1%) Medical School Year First 84 (11%) Second 155 (19%) Third 136 (17%) Fourth 266 (33%) Fifth 77 (9.6%) Sixth 81 (10%) University Hebron University 525 (66%) Al-Quds University 131 (16%) Palestine Polytechnic University 71 (8.9%) An-Najah National University 41 (5.1%) Arab American University 31 (3.9%) GPA Less than 2 (less than 60) 39 (4.9%) 2 to 2.5 (60 to 70) 68 (8.5%) 2.5 to 3 (70 to 80) 143 (18%) 3 to 3.5 (80 to 90) 441 (55%) 3.5 to 4 (90 to 100) 108 (14%) AI Awareness Yes 665 (83%) No 134 (17%) AI Experience Yes 584 (73%) No 215 (27%) Income Level Low (below basic needs) 109 (14%) Moderate (meets basic needs) 535 (67%) High (more than sufficient) 155 (19%) The overall readiness of participants to engage with AI in the medical field, as measured by the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), was moderate to high, with a median total readiness score of 73 (interquartile range [IQR]: 66–84) out of a possible 110. Among the four subdomains of the scale, the highest scores were reported in the ability domain (median: 27, IQR: 24–32) and cognition (median: 26, IQR: 23–29), indicating relatively strong confidence in both practical and theoretical AI competencies. The vision and ethics domains both yielded median scores of 10 (IQR: 9–12), suggesting comparatively lower awareness of AI’s broader implications and ethical considerations, though still within a moderate range (Table 2 ). Table 2 Subscale scores of AI readiness among Palestinian medical students (N = 799) MAIRS-MS Domain Median Interquartile Range (IQR) Minimum Maximum Cognition 26 23–29 8 40 Ability 27 24–32 8 40 Vision 10 9–12 3 15 Ethics 10 9–12 3 15 Total Readiness 73 66–84 22 110 Significant associations were observed between AI readiness and multiple demographic factors. Male students demonstrated significantly higher total readiness scores than their female counterparts (75.2 ± 11.6 vs. 71.4 ± 10.6, p < 0.001). This gender-based difference was consistent across all four domains, including ability (p = 0.002), cognition (p = 0.001), vision (p < 0.001), and ethics (p = 0.002). Age was also significantly associated with readiness. Younger students, particularly those aged 18, had notably lower readiness scores (67.9 ± 12.9), while older students aged 22 or more showed significantly higher scores across all domains (p < 0.001), indicating that AI familiarity may increase with maturity and academic progression (Table 3 ). Table 3 Association Between Sociodemographic Characteristics, Previous AI Experience, and AI Readiness Variable Cognition Ability Vision Ethics Medical AI Readiness Gender Male 10.5 (2.4) 26.4 (4.2) 27.9 (4.9) 10.4 (2.3) 75.2 (11.6) Female 9.9 (2.2) 25.4 (4.6) 26.0 (4.6) 10.0 (2.3) 71.4 (10.6) Age 18 9.4 (2.4) 23.7 (5.3) 25.0 (5.2) 9.8 (2.5) 67.9 (12.9) 19 9.8 (2.3) 25.6 (4.6) 26.0 (4.8) 9.7 (2.5) 71.1 (11.0) 20 10.0 (2.1) 26.5 (4.1) 26.3 (4.1) 10.2 (2.1) 73.0 (9.4) 21 10.3 (2.4) 26.0 (4.7) 27.6 (4.7) 10.2 (2.4) 73.9 (11.9) 22 11.4 (2.1) 27.1 (3.4) 29.7 (4.1) 11.1 (1.9) 79.2 (9.8) 23 9.5 (2.1) 25.5 (4.3) 25.5 (4.3) 9.8 (2.2) 70.3 (8.8) 24 or more 9.7 (2.1) 23.2 (5.4) 23.6 (5.3) 9.7 (2.5) 66.2 (12.7) Medial School Year First 9.5 (2.5) 24.4 (5.6) 25.8 (5.1) 10.0 (2.8) 69.7 (13.7) Second 9.7 (2.2) 25.4 (4.8) 26.0 (4.7) 9.8 (2.3) 71.0 (11.3) Third 10.1 (2.2) 26.9 (3.7) 26.6 (4.2) 10.2 (2.3) 73.9 (9.1) Fourth 11.3 (2.3) 26.7 (4.0) 28.6 (4.6) 10.8 (2.1) 77.5 (10.9) Fifth 9.4 (1.7) 25.6 (3.2) 27.3 (5.5) 10.1 (2.0) 72.4 (9.8) Sexth 9.6 (2.1) 24.6 (4.7) 25.6 (4.6) 9.5 (2.4) 69.2 (10.3) University Al-Quds university 10.3 (2.4) 25.5 (5.5) 27.6 (5.3) 10.5 (2.4) 74.0 (13.4) An-Najah National University 10.7 (2.1) 26.8 (5.7) 27.8 (5.4) 11.1 (2.3) 76.4 (13.8) Arab American University 10.8 (2.0) 27.1 (4.7) 28.6 (4.3) 10.9 (2.1) 77.5 (10.5) Hebron University 10.2 (2.4) 26.1 (3.9) 26.8 (4.7) 10.0 (2.3) 73.0 (10.5) Palestine Polytechnic University 10.0 (2.1) 24.6 (4.5) 27.0 (4.6) 10.4 (2.1) 72.0 (10.9) Awareness of AI usage No 8.8 (2.0) 23.6 (3.7) 23.8 (3.5) 9.2 (2.1) 65.4 (6.8) Yes 10.5 (2.3) 26.4 (4.4) 27.7 (4.8) 10.4 (2.3) 75.1 (11.3) Prior experience with AI in the medical field No 9.4 (2.4) 24.3 (4.0) 24.8 (4.3) 9.6 (2.2) 68.1 (9.4) Yes 10.5 (2.3) 26.6 (4.4) 27.9 (4.8) 10.4 (2.3) 75.4 (11.3) Grade Point Average GPA less than 2 (less than 60) 9.1 (2.2) 24.2 (4.3) 23.8 (3.7) 9.4 (2.2) 66.6 (7.3) 2 to 2.5 (60 to 70) 8.8 (1.9) 24.6 (4.0) 23.5 (4.4) 9.5 (2.4) 66.5 (7.9) 2.5 to 3 (70 to 80) 9.7 (2.3) 24.7 (4.9) 25.5 (4.6) 9.6 (2.4) 69.5 (11.8) 3 to 3.5 (80 to 90) 10.8 (2.2) 26.8 (3.8) 28.4 (4.3) 10.6 (2.2) 76.6 (10.3) 3.5 to 4 (90 to 100) 9.9 (2.4) 25.7 (5.4) 26.8 (5.5) 10.2 (2.5) 72.5 (12.7) How would you rate your income level Low (below basic needs) 9.6 (2.5) 23.8 (5.1) 24.0 (5.1) 9.4 (2.3) 66.9 (11.8) Moderate (sufficient to meet basic needs) 10.5 (2.3) 26.2 (3.9) 27.7 (4.5) 10.4 (2.2) 74.8 (10.6) High (more than sufficient for basic needs and additional expenses) 9.9 (2.2) 26.5 (5.1) 26.8 (5.0) 10.3 (2.5) 73.5 (11.7) Academic performance was positively correlated with AI readiness. Students with GPAs between 3.5 and 4.0 exhibited the highest levels of readiness, while those with lower GPAs reported lower scores in all readiness subdomains. This relationship was statistically significant (p < 0.001) and mirrored trends seen in related studies, suggesting that higher academic achievers are more likely to engage with emerging technologies. Similarly, income level played a notable role in shaping students’ readiness. Participants from high-income households scored significantly higher across all domains than their low-income peers, with readiness levels increasing progressively from low to high income categories (p < 0.001) (Table 3 ). Furthermore, both AI awareness and prior experience using AI in the medical field were strongly associated with higher readiness scores. Students who reported being aware of AI’s applications in healthcare demonstrated significantly greater preparedness across all domains, including cognition, ability, vision, and ethics (p < 0.001). Likewise, students with prior experience using AI technologies scored markedly higher in total readiness than those without experience (p < 0.001), underscoring the importance of exposure and hands-on interaction with AI tools in fostering medical students’ competence and confidence (Table 3 ). No statistically significant differences in AI readiness were found based on university affiliation, suggesting a relatively uniform level of preparedness across the participating institutions. Similarly, while descriptive data showed some variation in readiness across academic years, these differences were not statistically significant. This indicates that readiness to engage with AI may be influenced more by individual experience, GPA, and socioeconomic factors than by institutional or curricular differences within the sampled universities. Discussion Our findings demonstrate that Palestinian medical students exhibit a moderate to high level of readiness to engage with artificial intelligence (AI) in medicine, with a median total MAIRS-MS score of 73 (IQR: 66–84) out of 110. Notably, the ability (median: 27, IQR: 24–32) and cognition (median: 26, IQR: 23–29) domains scored highest, whereas vision and ethics domains both had lower median scores of 10 (IQR: 9–12). This pattern mirrors trends observed in the original MAIRS-MS validation by Karaca et al., which emphasized strong technical foundations but limited focus on ethical and strategic dimensions 10 . Similarly, a national survey of Jordanian students reported highest practical skills and lowest visionary scores, underscoring a global tendency to prioritize hands-on competencies over foresight 11 . The stronger performance in practical ability and cognitive understanding likely reflects current educational emphases that foreground AI’s immediate clinical applications such as diagnostic support and data interpretation while devoting comparatively less time to exploring AI’s broader implications. Buabbas et al. found that medical students globally are confident in operating AI tools but often lack structured grounding in ethical frameworks governing AI deployment, such as data privacy, algorithmic bias, and accountability 4 . The low ethics and vision scores in our cohort thus highlight a critical gap: without deeper engagement in AI’s societal and medico-legal implications, graduates may be unprepared to navigate the complex ethical terrain that increasingly accompanies AI-augmented practice. Demographic correlates further elucidate this landscape. Male students achieved significantly higher readiness scores than their female peers (75.2 ± 11.6 vs. 71.4 ± 10.6, p < 0.001), a finding consistent with Jordanian data showing greater self-reported confidence among males across all MAIRS-MS domains 11 , and aligning with a Saudi national survey in which male respondents expressed stronger interest and readiness for AI integration 12 . These gender disparities may stem from differential access to informal tech learning opportunities or cultural influences on self-efficacy in technology, underscoring the need for deliberate curricular strategies that foster equitable AI competency development across genders. Age and stage of training also played a significant role: students aged 22 or older and those in clinical years recorded higher readiness scores than younger, preclinical counterparts (p < 0.001). This maturational trajectory echoes findings by Cruz et al., who reported that senior medical and health science students worldwide exhibit more sophisticated understanding and application of AI concepts 13 , and is supported by Duan et al.’s comprehensive study linking increased educational exposure with enhanced AI attitudes and behavioral intentions among medical students 2 . Together, these patterns suggest that curricular interventions should be scaffolded progressively, introducing foundational AI concepts early and reinforcing practical and ethical competencies in later years. Academic performance, measured by cumulative GPA, was positively correlated with AI readiness (p < 0.001), reflecting the association of strong academic achievers with proactive engagement in emerging technologies. This relationship parallels Jordanian observations in which students with higher GPAs tended to report greater AI confidence, particularly in cognition and vision domains 11 , and may derive from the analytical skills and intellectual curiosity characteristic of high-performing students. Recognizing this link encourages the development of inclusive support systems that enable all students regardless of GPA to build AI literacy through targeted workshops, peer mentoring, and integrative assignments. Socioeconomic status emerged as another significant determinant: participants from high-income households outscored those from lower-income backgrounds across all domains (p < 0.001). This digital divide likely reflects disparities in access to computers, high-speed internet, and extracurricular tech education. Jebreen et al. documented similar patterns among Palestinian undergraduates, noting that resource-rich environments facilitate early exposure to AI tools and concepts, thereby enhancing readiness 14 . In contexts of constrained funding and faculty shortages, as described by Bhaya, Palestinian institutions face challenges in providing equitable AI learning resources, a reality that policy-makers must address to prevent widening competence gaps 15 . Unsurprisingly, prior AI awareness and hands-on experience were among the strongest predictors of readiness. Students who reported familiarity with AI applications in healthcare and those who had used AI platforms scored significantly higher on the MAIRS-MS (p < 0.001). This finding underscores the teachable nature of AI readiness: as Abyarjoo et al. demonstrated in their global mixed-methods study, even limited exposure to AI concepts substantially improves students’ confidence and adaptability 16 . Embedding practical AI modules such as supervised use of diagnostic algorithms or interactive coding sessions could therefore yield immediate gains in readiness. Our analysis found no significant differences in AI readiness across the five surveyed universities (p > 0.05), suggesting a systemic rather than institution-specific shortfall in AI education within Palestinian medical schools in the West Bank. This homogeneity parallels Jordanian results indicating uniform readiness levels across schools 11 , and aligns with Indonesian findings showing similar domain patterns at Pelita Harapan University 17 . Such consistency presents an opportunity for national-level curricular reforms: standardized AI modules could be developed centrally and deployed across all institutions, ensuring equitable preparation without redundant curricular design efforts. Interpreting these results demands appreciation of the Palestinian context, where chronic conflict and resource scarcity shape educational experiences. Travel restrictions and checkpoint delays impose logistical burdens on students, yet crises have also galvanized digital ingenuity. During the COVID-19 pandemic, Gaza’s medical students dramatically increased e-learning engagement, with over 65% reporting more than seven hours of online study per week when in-person classes were suspended up from just a few hours pre-pandemic 18 . This resilience demonstrates strong baseline digital literacy that likely underpins our cohort’s solid ability and cognition scores. Similarly, Palestinian tech entrepreneurs have harnessed AI to address local challenges such as monitoring hate speech illustrating an adaptive innovation culture even under duress 15 . The integration of artificial intelligence (AI) into medical education in Palestine must be contextualized within the broader systemic constraints that impede academic advancement in conflict-affected regions. As underscored by Hanifa and Amro (2024), researchers in Palestine encounter profound obstacles, including limited research infrastructure, insufficient funding, and severe movement restrictions factors that collectively hinder the development, execution, and dissemination of scientific research 19 . These structural challenges not only impact the volume and visibility of academic output but also restrict opportunities for incorporating emerging technologies such as AI into educational frameworks. Nevertheless, the persistent efforts of Palestinian academic institutions and scholars, despite these adversities, reflect a notable degree of resilience and a strong commitment to advancing medical education. Recognizing and addressing these contextual limitations is essential for the equitable and sustainable implementation of AI-driven innovations in low-resource and conflict-affected settings. These insights carry urgent implications for medical education in Palestine. Curricula must evolve beyond algorithmic instruction to integrate case-based discussions on AI’s ethical and future-oriented dimensions. For example, simulated scenarios highlighting algorithmic bias or data-privacy breaches could foster critical reflection, while dedicated workshops on regulatory frameworks and medico-legal issues would bolster the ethics domain. Aligning with Karaca et al.’s call for comprehensive AI literacy, such initiatives should be embedded longitudinally from preclinical lectures on AI fundamentals to clinical rotations utilizing decision-support tools with evaluation metrics tied to MAIRS-MS outcomes 10 . At the policy level, the uniform readiness landscape supports a centralized approach: the Ministry of Higher Education and accrediting bodies could convene a task force to develop a national AI competency framework, incorporating standardized modules, faculty development programs, and shared digital resources 14 . Collaborative partnerships with computer science departments, international institutions, and non-governmental organizations could supply technical expertise and funding, mitigating local resource constraints. By articulating clear competency milestones and providing open-access materials, stakeholders can ensure that all seven Palestinian medical schools equip students to leverage AI safely and ethically. Our study has limitations. Its cross-sectional design captures readiness at a single time point and relies on self-reported perceptions, which may not reflect actual competencies. The MAIRS-MS measures perceived preparedness rather than objective skill proficiency, and causality cannot be inferred from observed associations. Furthermore, although the sample included all universities in the West Bank, Palestine, that offer medical education, the overrepresentation of students from Hebron University (66%) may limit the generalizability of the findings. Future research should address these gaps by incorporating objective assessments such as practical AI tasks and pursuing longitudinal designs to track readiness evolution and its impact on clinical practice. Building on these findings, future studies might evaluate targeted educational interventions (e.g., AI workshops or ethics seminars) using pre- and post-MAIRS-MS measures to determine efficacy. Qualitative inquiries through focus groups or interviews could illuminate student perspectives on curriculum content, barriers to engagement, and resource needs. Investigating faculty readiness and institutional readiness for AI integration would further enrich our understanding of the educational ecosystem. Finally, exploring the role of emergent technologies such as large language models in medical learning environments could reveal new pathways to enhance AI literacy and prepare students for the evolving digital landscape. By addressing the identified gaps in vision and ethics, leveraging students’ demonstrated technical strengths, and implementing coordinated, resource-savvy reforms, Palestinian medical education can nurture a new generation of physicians equipped to harness AI for improved patient care, even amid ongoing regional challenges. Conclusion Our study reveals that Palestinian medical students possess a solid foundation in the practical and cognitive dimensions of artificial intelligence, yet demonstrate notable deficits in ethical reasoning and strategic foresight. Demographic factors including gender, age, academic performance, and socioeconomic status as well as prior AI exposure, significantly influence readiness levels. The uniform readiness across universities suggests that curricular gaps are systemic rather than institution-specific, underscoring the need for a coordinated, national strategy. To prepare future physicians for an AI-augmented healthcare landscape, Palestinian medical curricula must evolve to integrate ethics, medico-legal discussions, and visionary case-based learning alongside hands-on technical training. Centralized development of standardized AI modules, supported by faculty development and inter-institutional collaboration, can ensure equitable and comprehensive AI literacy. Future research should employ longitudinal designs, objective skill assessments, and qualitative explorations to evaluate the impact of targeted interventions and to track readiness progression. By addressing both the strengths and gaps identified, stakeholders can equip Palestinian medical graduates to harness AI’s potential responsibly and effectively, ultimately enhancing patient care in a resource-constrained and conflict-affected setting. Declarations -Data availability : The data generated in this study are available upon request from the corresponding author. -Acknowledgements: We would like to express our sincere gratitude to the medical students who provided invaluable assistance in the data collection process for this study. In particular, we extend our appreciation to Haya Alshaikh, Ibrahim Al-Qunibi, Dana Sami, and Ansam Hirbawe for their dedication, commitment, and attention to detail. Their efforts significantly contributed to the quality and completeness of the data, and we are grateful for their support throughout this project. -Funding: There are no sources of funding to declare. -Author contributions: Hamdah Hanifa, Alhareth M. Amro, and Salahaldeen Deeb contributed equally to the conception and design of the study, as well as to the writing and editing of both the initial and final versions of the manuscript. Ammir Abuzahra and Khaled Alhashlamon were responsible for data interpretation and contributed to the critical review of the manuscript. Tarek A. Owais performed the data analysis and provided methodological guidance. Yousef Abu Ayesh contributed to the methodology, and Nihad Assaf provided supervision. All authors read and approved the final manuscript. -Ethics approval and consent to participate: All procedures performed in this study involving human participants complied with the institutional and/or national research committee ethical standards and the 1964 Helsinki declaration and subsequent amendments or equivalent ethical standards. The study was designed and conducted in accordance with the ethical principles established by Al-Quds University. Therefore, ethical approval was obtained from the Institutional Review Board Committee, Faculty of Medicine, Al-Quds University. Written informed consent was obtained from all the participants for the participation of this study and accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal on request. -Clinical Trial Number: Not applicable. -Consent for publication: Not applicable. -Competing interests: The authors declare no competing interests. References Li Q, Qin Y. AI in medical education: medical student perception, curriculum recommendations and design suggestions. BMC Med Educ . 2023;23(1):852. doi:10.1186/s12909-023-04700-8 Duan S, Liu C, Rong T, Zhao Y, Liu B. Integrating AI in medical education: a comprehensive study of medical students’ attitudes, concerns, and behavioral intentions. BMC Med Educ . 2025;25(1):599. doi:10.1186/s12909-025-07177-9 Sami A, Tanveer F, Sajwani K, et al. 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J Med Educ Curric Dev . 2024;11:23821205241281650. doi:10.1177/23821205241281648 Al Shahrani A, Alhumaidan N, AlHindawi Z, et al. Readiness to Embrace Artificial Intelligence Among Medical Students in Saudi Arabia: A National Survey. Healthc . 2024;12(24):1-13. doi:10.3390/healthcare12242504 Cruz JP, Sembekova A, Omirzakova D, Bolla SR, Balay-odao EM. Attitudes Toward and Readiness for Medical Artificial Intelligence Among Medical and Health Science Students. Heal Prof Educ . 2024;10(3):274-287. doi:10.55890/2452-3011.1296 Jebreen K, Radwan E, Kammoun-Rebai W, et al. Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine. BMC Med Educ . 2024;24(1):1-19. doi:10.1186/s12909-024-05465-4 Bhaya AG. AI in times of conflict: How Palestinian entrepreneurs fight on. Published online 2025. https://www.trtworld.com/middle-east/ai-in-times-of-conflict-how-palestinian-entrepreneurs-fight-on-18268540 Ejaz H, McGrath H, Wong BL, Guise A, Vercauteren T, Shapey J. Artificial intelligence and medical education: A global mixed-methods study of medical students’ perspectives. Digit Heal . 2022;8:20552076221089100. doi:10.1177/20552076221089099 Lugito NPH, Cucunawangsih C, Suryadinata N, et al. Readiness, knowledge, and perception towards artificial intelligence of medical students at faculty of medicine, Pelita Harapan University, Indonesia: a cross sectional study. BMC Med Educ . 2024;24(1):1044. doi:10.1186/s12909-024-06058-x Ismail A, Ismail A, Alazar A, Saman M, Abu-Elqomboz A, Sharaf FK. E-Learning Medical Education in Gaza During COVID-19: Students’ Experiences and Policy Recommendations. J Med Educ Curric Dev . 2023;10:23821205231164228. doi:10.1177/23821205231164228 Hanifa H, Amro AM. Promoting equity in medical research: ensuring access to publishing opportunities for researchers in Syria and Palestine. BMC Med Educ . 2024;24(1):1371. doi:10.1186/s12909-024-06375-1 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6623487","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471466036,"identity":"71bdd775-aa25-41b6-b3a8-52a33bedf273","order_by":0,"name":"Hamdah Hanifa","email":"","orcid":"","institution":"University of Kalamoon","correspondingAuthor":false,"prefix":"","firstName":"Hamdah","middleName":"","lastName":"Hanifa","suffix":""},{"id":471466038,"identity":"e2a5af95-edbf-43c2-8d1f-07d5e07c052e","order_by":1,"name":"Alhareth M. Amro","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDACdh4wlQDhVQAxM3MDfi3MYC0GUC1nQCKMpGhhbAOT+LXwN/Me/FxR8SePgf102oeP82qj+duBWn5UbMOpReIwX7LkmTMGxQw8uZtnztx2PHfGYcYGxp4zt3Fbc5jHQLKxzSCxgSF3MzPvtmO5DUAtzIxtuLXIH+Yx/tn4D6iF/y1Qy5xjufMJaTE4zGMm2dgA1CIBsqWhJncDIS2GQC2WDceME9sk3m5mnHHsQO5GoJaD+Pwid7zH+GZDjVxiP3/uZoYPNXW5884fPvjgRwUe78MAGzQ0wOQBwuoRoI4UxaNgFIyCUTBCAAA5OFkBvm3CfQAAAABJRU5ErkJggg==","orcid":"","institution":"Al-Quds University","correspondingAuthor":true,"prefix":"","firstName":"Alhareth","middleName":"M.","lastName":"Amro","suffix":""},{"id":471466041,"identity":"f2243997-544c-425d-a2a9-97b3b4de4ddf","order_by":2,"name":"Salahaldeen Deeb","email":"","orcid":"","institution":"Al-Quds University","correspondingAuthor":false,"prefix":"","firstName":"Salahaldeen","middleName":"","lastName":"Deeb","suffix":""},{"id":471466045,"identity":"a00aaa78-a441-4122-a51d-ffcb60bc1120","order_by":3,"name":"Ammir Abuzahra","email":"","orcid":"","institution":"Hebron University","correspondingAuthor":false,"prefix":"","firstName":"Ammir","middleName":"","lastName":"Abuzahra","suffix":""},{"id":471466046,"identity":"ae4feca2-5a50-4d0c-82c6-dd069dd07a17","order_by":4,"name":"Khaled Alhashlamon","email":"","orcid":"","institution":"Al-Quds University","correspondingAuthor":false,"prefix":"","firstName":"Khaled","middleName":"","lastName":"Alhashlamon","suffix":""},{"id":471466051,"identity":"2e4e7b3e-ea2a-4e96-ab88-ad1e43ef1f4f","order_by":5,"name":"Tarek A. Owais","email":"","orcid":"","institution":"Benisuef University","correspondingAuthor":false,"prefix":"","firstName":"Tarek","middleName":"A.","lastName":"Owais","suffix":""},{"id":471466052,"identity":"ba9ef3c3-d5af-47dd-82ad-3dd8e716c87a","order_by":6,"name":"Yousef Abu Ayesh","email":"","orcid":"","institution":"An-Najah National University","correspondingAuthor":false,"prefix":"","firstName":"Yousef","middleName":"Abu","lastName":"Ayesh","suffix":""},{"id":471466053,"identity":"3b2ed35d-a261-4f4f-aeab-b9ce3abb5231","order_by":7,"name":"Nihad Assaf","email":"","orcid":"","institution":"CES De Nephrologie, University of Kalamoon","correspondingAuthor":false,"prefix":"","firstName":"Nihad","middleName":"","lastName":"Assaf","suffix":""}],"badges":[],"createdAt":"2025-05-08 21:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6623487/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6623487/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108987046,"identity":"b3354e66-8155-496d-aba8-1233fafa468c","added_by":"auto","created_at":"2026-05-11 12:58:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":411837,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6623487/v1/b8c49b38-bd98-44af-b7c8-a2c75f081a82.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Embracing Artificial Intelligence: Evaluating Technological Adaptability in Palestinian Medical Education","fulltext":[{"header":"Background","content":"\u003cp\u003eArtificial intelligence (AI) is scientifically defined as the ability of computing systems to understand and leverage given algorithms to analyze data to achieve specific goals, as highlighted by Kaplan and Haenlein. China is one of the leading countries in adopting an advanced approach based on AI and its various tools in the medical field. Its applications include medical data analysis, interpretation of radiographic images, and achieving diagnosis and treatment\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Even at the university level, the digital revolution of AI has become evident among students, especially medical students who have relied on applications like DeepSeek, Chat GPT, and Copilot to understand medical materials, translate texts, solve scientific questions, and explain the complex mechanisms of diseases \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Recent statistics indicate that the number of Chat GPT users has exceeded 100\u0026nbsp;million in just two months, reflecting its wide-ranging reliance in many fields \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Furthermore, machine learning (ML) has gained widespread acceptance among medical researchers due to its advanced capabilities, such as predicting five-year survival rates in cancer patients \u003csup\u003e4567\u003c/sup\u003e. The multiple benefits of AI require healthcare workers and students to develop skills in using this technology in their specialties in a way that serves patients and improves medical quality \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, companies like Epic, Amazon, and Google have expressed objections to some of the proposed regulations from the United States to regulate the use of AI in healthcare technologies \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This study aims to assess the level of awareness and engagement with AI technologies among Palestinian medical students enrolled in universities in the West Bank, Palestine. It is considered the first study of its kind to delve into this field, providing a comprehensive overview of the potential application of AI amid the challenges of wars and conflicts in the region.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Participants:\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted to assess the readiness of medical students to utilize artificial intelligence technologies in the medical field. The study included 799 medical students from all universities in the West Bank, Palestine, that have a Faculty of Medicine, specifically: Al-Quds University, An-Najah National University, Hebron University, Palestine Polytechnic University, and Arab American University. Involving students from all universities aimed to enhance the possibility of generalizing the results within a single national context, while ensuring a more homogeneous assessment of artificial intelligence readiness. The participants represented all stages of medical education, from basic sciences to clinical years. This comprehensive sampling strategy facilitated a detailed analysis of artificial intelligence readiness across different academic levels, providing valuable insights into the advanced competencies of future healthcare professionals.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection Procedures:\u003c/h3\u003e\n\u003cp\u003eThe study adopted a flexible approach combining electronic and paper questionnaires to ensure maximum participation. Questionnaires were distributed through official channels of medical colleges, as well as communication platforms such as WhatsApp groups and academic channels on Telegram. To enhance response rates, questionnaires were also distributed manually after lectures in classrooms, with quiet locations like libraries or university cafes provided to allow students to respond with focus. The questionnaire used in this study was adapted from the work by Karaca, O et al. (2021) titled \u003cem\u003e\" Medical artificial intelligence readiness scale for medical students (MAIRS-MS) – development, validity and reliability study\"\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrior to commencement, all participants underwent a brief instructional session (3–5 minutes) where the study's objectives and significance were explained, ensuring complete data confidentiality. The research team emphasized direct interaction with students to address any inquiries, enhancing trust and active participation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExclusion Criteria\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eStudents not enrolled in the relevant human medicine college under study.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eParticipants who declined consent or withdrew before completing the questionnaire.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePartial or incomplete responses (less than 90% of the questions).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eData collection spanned from February 2 to April 20, 2025, to ensure comprehensive coverage across classes and examination periods. The average questionnaire response time was 8 minutes, with clear written and auditory instructions provided (through audio links for the electronic version) to simplify the process. Authors were assigned in each university to oversee the distribution and responses, with any qualitative research notes recorded.\u003c/p\u003e\n\u003ch3\u003eEthical Considerations:\u003c/h3\u003e\n\u003cp\u003e The study adhered to the highest standards of academic integrity and ethical protection for participants. All data underwent a three-stage review process:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003e\u003c/b\u003eAutomated screening using outlier detection software.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eManual review by the primary research team.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e20% random review by an independent supervisor.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003c/p\u003e\u003cp\u003e was obtained from the Institutional Review Board (IRB) at Al-Quds University.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eQuestionnaire:\u003c/h3\u003e\n\u003cp\u003eThe study adopted the validated scientific tool \"Medical Artificial Intelligence Readiness Scale for Medical Students\" (MAIRS-MS), a multidimensional diagnostic tool consisting of 22 items distributed across four main axes:\u003c/p\u003e \u003cp\u003e \u003cb\u003eEthical\u003c/b\u003e Axis (3 items): Assessing awareness of ethical challenges.\u003c/p\u003e \u003cp\u003e \u003cb\u003eVisionary\u003c/b\u003e Axis (3 items): Measuring the inclination towards technology adoption.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAbility\u003c/b\u003e Axis (8 items): Evaluating practical skills.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCognitive\u003c/b\u003e Understanding Axis (8 items): Assessing theoretical understanding.\u003c/p\u003e \u003cp\u003eIt utilized a 5-point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree). Total Score Range: 22–110 points (higher scores indicate higher readiness). The scale demonstrated high reliability (α = 0.87) in previous studies \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe tool underwent rigorous adaptation processes including double translation (Arabic-English-Arabic), expert review, and a pilot study on a small sample (n = 30).\u003c/p\u003e \u003cp\u003e \u003cb\u003eQuestionnaire Structure\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003cb\u003e1) Introductory Section\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eDemographic data (gender, age, academic year…).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAcademic indicators (cumulative GPA).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePrevious experience with artificial intelligence.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003e2) Main Section\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eMAIRS-MS questions designed in a hierarchical sequence.\u003c/p\u003e \u003cp\u003eThis tool was specifically developed to measure technical readiness in medical educational settings, taking into consideration the cultural characteristics of the target sample, making it a comprehensive and accurate assessment tool.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis:\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS Inc software, version 22. Descriptive statistics, including frequencies and percentages, were employed to summarize the participants' demographic characteristics. To examine differences in AI readiness levels between groups (e.g., participants with and without prior AI experience), independent samples *t*-tests were conducted. Assumptions of normality and homogeneity of variance were verified prior to analysis.\u003c/p\u003e \u003c/div\u003e "},{"header":"Result","content":"\u003cp\u003eA total of 799 medical students from five Palestinian universities participated in this cross-sectional study. The majority were from Hebron University (66%), followed by Al-Quds University (16%), Palestine Polytechnic University (8.9%), An-Najah National University (5.1%), and Arab American University (3.9%). The gender distribution was relatively balanced, with 53% identifying as male and 47% as female. Participants were drawn from all six academic years, with the largest representation from the fourth year (33%). Students ranged in age from 18 to 24 years or older, with 22-year-olds comprising the largest age group (27%). Most students reported a GPA between 3.0 and 3.5 (55%), with 14% exceeding 3.5, and 18% reporting a GPA between 2.5 and 3.0. Additionally, 83% of the students indicated awareness of artificial intelligence (AI) applications in medicine, and 73% had prior experience using AI-related tools or platforms in a medical context. Regarding socioeconomic status, the majority of participants reported a moderate-income level (67%), followed by high income (19%) and low income (14%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Characteristics of Participants (N = 799)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubcategory\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e426 (53%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e373 (47%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (9.1%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157 (20%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (16%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (14%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216 (27%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (12%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 or more\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (3.1%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eMedical School Year\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (11%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecond\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 (19%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThird\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (17%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFourth\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e266 (33%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFifth\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (9.6%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSixth\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (10%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHebron University\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e525 (66%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl-Quds University\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131 (16%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePalestine Polytechnic University\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (8.9%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAn-Najah National University\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (5.1%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArab American University\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (3.9%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eGPA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 2 (less than 60)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (4.9%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 to 2.5 (60 to 70)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (8.5%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5 to 3 (70 to 80)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143 (18%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 to 3.5 (80 to 90)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e441 (55%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5 to 4 (90 to 100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (14%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAI Awareness\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e665 (83%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134 (17%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAI Experience\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e584 (73%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e215 (27%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIncome Level\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow (below basic needs)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (14%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate (meets basic needs)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e535 (67%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh (more than sufficient)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 (19%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe overall readiness of participants to engage with AI in the medical field, as measured by the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), was moderate to high, with a median total readiness score of 73 (interquartile range [IQR]: 66–84) out of a possible 110. Among the four subdomains of the scale, the highest scores were reported in the ability domain (median: 27, IQR: 24–32) and cognition (median: 26, IQR: 23–29), indicating relatively strong confidence in both practical and theoretical AI competencies. The vision and ethics domains both yielded median scores of 10 (IQR: 9–12), suggesting comparatively lower awareness of AI’s broader implications and ethical considerations, though still within a moderate range (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubscale scores of AI readiness among Palestinian medical students (N = 799)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAIRS-MS Domain\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterquartile Range (IQR)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognition\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23–29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbility\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24–32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVision\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9–12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthics\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9–12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Readiness\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66–84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eSignificant associations were observed between AI readiness and multiple demographic factors. Male students demonstrated significantly higher total readiness scores than their female counterparts (75.2 ± 11.6 vs. 71.4 ± 10.6, p \u0026lt; 0.001). This gender-based difference was consistent across all four domains, including ability (p = 0.002), cognition (p = 0.001), vision (p \u0026lt; 0.001), and ethics (p = 0.002). Age was also significantly associated with readiness. Younger students, particularly those aged 18, had notably lower readiness scores (67.9 ± 12.9), while older students aged 22 or more showed significantly higher scores across all domains (p \u0026lt; 0.001), indicating that AI familiarity may increase with maturity and academic progression (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation Between Sociodemographic Characteristics, Previous AI Experience, and AI Readiness\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCognition\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbility\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVision\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEthics\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedical AI Readiness\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.5 (2.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.4 (4.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.9 (4.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.4 (2.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.2 (11.6)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.9 (2.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.4 (4.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.0 (4.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.0 (2.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.4 (10.6)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.4 (2.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.7 (5.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.0 (5.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.8 (2.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.9 (12.9)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.8 (2.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.6 (4.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.0 (4.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.7 (2.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.1 (11.0)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0 (2.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.5 (4.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.3 (4.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.2 (2.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.0 (9.4)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.3 (2.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.0 (4.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.6 (4.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.2 (2.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.9 (11.9)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.4 (2.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.1 (3.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.7 (4.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.1 (1.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79.2 (9.8)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.5 (2.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.5 (4.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.5 (4.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.8 (2.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.3 (8.8)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24 or more\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.7 (2.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.2 (5.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.6 (5.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.7 (2.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.2 (12.7)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eMedial School Year\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.5 (2.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.4 (5.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.8 (5.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.0 (2.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.7 (13.7)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.7 (2.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.4 (4.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.0 (4.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.8 (2.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.0 (11.3)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThird\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.1 (2.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.9 (3.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.6 (4.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.2 (2.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.9 (9.1)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFourth\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.3 (2.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.7 (4.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.6 (4.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.8 (2.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.5 (10.9)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFifth\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.4 (1.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.6 (3.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.3 (5.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.1 (2.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.4 (9.8)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSexth\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.6 (2.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.6 (4.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.6 (4.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.5 (2.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.2 (10.3)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl-Quds university\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.3 (2.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.5 (5.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.6 (5.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.5 (2.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74.0 (13.4)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAn-Najah National University\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.7 (2.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.8 (5.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.8 (5.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.1 (2.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.4 (13.8)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArab American University\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.8 (2.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.1 (4.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.6 (4.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.9 (2.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.5 (10.5)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHebron University\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.2 (2.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.1 (3.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.8 (4.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.0 (2.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.0 (10.5)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePalestine Polytechnic University\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0 (2.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.6 (4.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.0 (4.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.4 (2.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.0 (10.9)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eAwareness of AI usage\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.8 (2.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.6 (3.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.8 (3.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.2 (2.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.4 (6.8)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.5 (2.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.4 (4.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.7 (4.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.4 (2.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.1 (11.3)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ePrior experience with AI in the medical field\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.4 (2.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.3 (4.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.8 (4.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.6 (2.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.1 (9.4)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.5 (2.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.6 (4.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.9 (4.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.4 (2.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.4 (11.3)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eGrade Point Average\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPA less than 2 (less than 60)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.1 (2.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.2 (4.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.8 (3.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.4 (2.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.6 (7.3)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 to 2.5 (60 to 70)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.8 (1.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.6 (4.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.5 (4.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.5 (2.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.5 (7.9)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.5 to 3 (70 to 80)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.7 (2.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.7 (4.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.5 (4.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.6 (2.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.5 (11.8)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 to 3.5 (80 to 90)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.8 (2.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.8 (3.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.4 (4.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.6 (2.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.6 (10.3)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.5 to 4 (90 to 100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.9 (2.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.7 (5.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.8 (5.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.2 (2.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.5 (12.7)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eHow would you rate your income level\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow (below basic needs)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.6 (2.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.8 (5.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.0 (5.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.4 (2.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.9 (11.8)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate (sufficient to meet basic needs)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.5 (2.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.2 (3.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.7 (4.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.4 (2.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74.8 (10.6)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (more than sufficient for basic needs and additional expenses)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.9 (2.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.5 (5.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.8 (5.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.3 (2.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.5 (11.7)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eAcademic performance was positively correlated with AI readiness. Students with GPAs between 3.5 and 4.0 exhibited the highest levels of readiness, while those with lower GPAs reported lower scores in all readiness subdomains. This relationship was statistically significant (p \u0026lt; 0.001) and mirrored trends seen in related studies, suggesting that higher academic achievers are more likely to engage with emerging technologies. Similarly, income level played a notable role in shaping students’ readiness. Participants from high-income households scored significantly higher across all domains than their low-income peers, with readiness levels increasing progressively from low to high income categories (p \u0026lt; 0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, both AI awareness and prior experience using AI in the medical field were strongly associated with higher readiness scores. Students who reported being aware of AI’s applications in healthcare demonstrated significantly greater preparedness across all domains, including cognition, ability, vision, and ethics (p \u0026lt; 0.001). Likewise, students with prior experience using AI technologies scored markedly higher in total readiness than those without experience (p \u0026lt; 0.001), underscoring the importance of exposure and hands-on interaction with AI tools in fostering medical students’ competence and confidence (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNo statistically significant differences in AI readiness were found based on university affiliation, suggesting a relatively uniform level of preparedness across the participating institutions. Similarly, while descriptive data showed some variation in readiness across academic years, these differences were not statistically significant. This indicates that readiness to engage with AI may be influenced more by individual experience, GPA, and socioeconomic factors than by institutional or curricular differences within the sampled universities.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings demonstrate that Palestinian medical students exhibit a moderate to high level of readiness to engage with artificial intelligence (AI) in medicine, with a median total MAIRS-MS score of 73 (IQR: 66\u0026ndash;84) out of 110. Notably, the ability (median: 27, IQR: 24\u0026ndash;32) and cognition (median: 26, IQR: 23\u0026ndash;29) domains scored highest, whereas vision and ethics domains both had lower median scores of 10 (IQR: 9\u0026ndash;12). This pattern mirrors trends observed in the original MAIRS-MS validation by Karaca et al., which emphasized strong technical foundations but limited focus on ethical and strategic dimensions\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Similarly, a national survey of Jordanian students reported highest practical skills and lowest visionary scores, underscoring a global tendency to prioritize hands-on competencies over foresight\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe stronger performance in practical ability and cognitive understanding likely reflects current educational emphases that foreground AI\u0026rsquo;s immediate clinical applications such as diagnostic support and data interpretation while devoting comparatively less time to exploring AI\u0026rsquo;s broader implications. Buabbas et al. found that medical students globally are confident in operating AI tools but often lack structured grounding in ethical frameworks governing AI deployment, such as data privacy, algorithmic bias, and accountability\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The low ethics and vision scores in our cohort thus highlight a critical gap: without deeper engagement in AI\u0026rsquo;s societal and medico-legal implications, graduates may be unprepared to navigate the complex ethical terrain that increasingly accompanies AI-augmented practice.\u003c/p\u003e \u003cp\u003eDemographic correlates further elucidate this landscape. Male students achieved significantly higher readiness scores than their female peers (75.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6 vs. 71.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a finding consistent with Jordanian data showing greater self-reported confidence among males across all MAIRS-MS domains\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and aligning with a Saudi national survey in which male respondents expressed stronger interest and readiness for AI integration\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. These gender disparities may stem from differential access to informal tech learning opportunities or cultural influences on self-efficacy in technology, underscoring the need for deliberate curricular strategies that foster equitable AI competency development across genders.\u003c/p\u003e \u003cp\u003eAge and stage of training also played a significant role: students aged 22 or older and those in clinical years recorded higher readiness scores than younger, preclinical counterparts (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This maturational trajectory echoes findings by Cruz et al., who reported that senior medical and health science students worldwide exhibit more sophisticated understanding and application of AI concepts\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and is supported by Duan et al.\u0026rsquo;s comprehensive study linking increased educational exposure with enhanced AI attitudes and behavioral intentions among medical students\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Together, these patterns suggest that curricular interventions should be scaffolded progressively, introducing foundational AI concepts early and reinforcing practical and ethical competencies in later years.\u003c/p\u003e \u003cp\u003eAcademic performance, measured by cumulative GPA, was positively correlated with AI readiness (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reflecting the association of strong academic achievers with proactive engagement in emerging technologies. This relationship parallels Jordanian observations in which students with higher GPAs tended to report greater AI confidence, particularly in cognition and vision domains\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and may derive from the analytical skills and intellectual curiosity characteristic of high-performing students. Recognizing this link encourages the development of inclusive support systems that enable all students regardless of GPA to build AI literacy through targeted workshops, peer mentoring, and integrative assignments.\u003c/p\u003e \u003cp\u003eSocioeconomic status emerged as another significant determinant: participants from high-income households outscored those from lower-income backgrounds across all domains (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This digital divide likely reflects disparities in access to computers, high-speed internet, and extracurricular tech education. Jebreen et al. documented similar patterns among Palestinian undergraduates, noting that resource-rich environments facilitate early exposure to AI tools and concepts, thereby enhancing readiness\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In contexts of constrained funding and faculty shortages, as described by Bhaya, Palestinian institutions face challenges in providing equitable AI learning resources, a reality that policy-makers must address to prevent widening competence gaps\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUnsurprisingly, prior AI awareness and hands-on experience were among the strongest predictors of readiness. Students who reported familiarity with AI applications in healthcare and those who had used AI platforms scored significantly higher on the MAIRS-MS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding underscores the teachable nature of AI readiness: as Abyarjoo et al. demonstrated in their global mixed-methods study, even limited exposure to AI concepts substantially improves students\u0026rsquo; confidence and adaptability\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Embedding practical AI modules such as supervised use of diagnostic algorithms or interactive coding sessions could therefore yield immediate gains in readiness.\u003c/p\u003e \u003cp\u003eOur analysis found no significant differences in AI readiness across the five surveyed universities (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting a systemic rather than institution-specific shortfall in AI education within Palestinian medical schools in the West Bank. This homogeneity parallels Jordanian results indicating uniform readiness levels across schools\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and aligns with Indonesian findings showing similar domain patterns at Pelita Harapan University\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Such consistency presents an opportunity for national-level curricular reforms: standardized AI modules could be developed centrally and deployed across all institutions, ensuring equitable preparation without redundant curricular design efforts.\u003c/p\u003e \u003cp\u003eInterpreting these results demands appreciation of the Palestinian context, where chronic conflict and resource scarcity shape educational experiences. Travel restrictions and checkpoint delays impose logistical burdens on students, yet crises have also galvanized digital ingenuity. During the COVID-19 pandemic, Gaza\u0026rsquo;s medical students dramatically increased e-learning engagement, with over 65% reporting more than seven hours of online study per week when in-person classes were suspended up from just a few hours pre-pandemic\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This resilience demonstrates strong baseline digital literacy that likely underpins our cohort\u0026rsquo;s solid ability and cognition scores. Similarly, Palestinian tech entrepreneurs have harnessed AI to address local challenges such as monitoring hate speech illustrating an adaptive innovation culture even under duress\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe integration of artificial intelligence (AI) into medical education in Palestine must be contextualized within the broader systemic constraints that impede academic advancement in conflict-affected regions. As underscored by Hanifa and Amro (2024), researchers in Palestine encounter profound obstacles, including limited research infrastructure, insufficient funding, and severe movement restrictions factors that collectively hinder the development, execution, and dissemination of scientific research\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. These structural challenges not only impact the volume and visibility of academic output but also restrict opportunities for incorporating emerging technologies such as AI into educational frameworks. Nevertheless, the persistent efforts of Palestinian academic institutions and scholars, despite these adversities, reflect a notable degree of resilience and a strong commitment to advancing medical education. Recognizing and addressing these contextual limitations is essential for the equitable and sustainable implementation of AI-driven innovations in low-resource and conflict-affected settings.\u003c/p\u003e \u003cp\u003eThese insights carry urgent implications for medical education in Palestine. Curricula must evolve beyond algorithmic instruction to integrate case-based discussions on AI\u0026rsquo;s ethical and future-oriented dimensions. For example, simulated scenarios highlighting algorithmic bias or data-privacy breaches could foster critical reflection, while dedicated workshops on regulatory frameworks and medico-legal issues would bolster the ethics domain. Aligning with Karaca et al.\u0026rsquo;s call for comprehensive AI literacy, such initiatives should be embedded longitudinally from preclinical lectures on AI fundamentals to clinical rotations utilizing decision-support tools with evaluation metrics tied to MAIRS-MS outcomes\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt the policy level, the uniform readiness landscape supports a centralized approach: the Ministry of Higher Education and accrediting bodies could convene a task force to develop a national AI competency framework, incorporating standardized modules, faculty development programs, and shared digital resources\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Collaborative partnerships with computer science departments, international institutions, and non-governmental organizations could supply technical expertise and funding, mitigating local resource constraints. By articulating clear competency milestones and providing open-access materials, stakeholders can ensure that all seven Palestinian medical schools equip students to leverage AI safely and ethically.\u003c/p\u003e \u003cp\u003eOur study has limitations. Its cross-sectional design captures readiness at a single time point and relies on self-reported perceptions, which may not reflect actual competencies. The MAIRS-MS measures perceived preparedness rather than objective skill proficiency, and causality cannot be inferred from observed associations. Furthermore, although the sample included all universities in the West Bank, Palestine, that offer medical education, the overrepresentation of students from Hebron University (66%) may limit the generalizability of the findings. Future research should address these gaps by incorporating objective assessments such as practical AI tasks and pursuing longitudinal designs to track readiness evolution and its impact on clinical practice.\u003c/p\u003e \u003cp\u003eBuilding on these findings, future studies might evaluate targeted educational interventions (e.g., AI workshops or ethics seminars) using pre- and post-MAIRS-MS measures to determine efficacy. Qualitative inquiries through focus groups or interviews could illuminate student perspectives on curriculum content, barriers to engagement, and resource needs. Investigating faculty readiness and institutional readiness for AI integration would further enrich our understanding of the educational ecosystem. Finally, exploring the role of emergent technologies such as large language models in medical learning environments could reveal new pathways to enhance AI literacy and prepare students for the evolving digital landscape.\u003c/p\u003e \u003cp\u003e By addressing the identified gaps in vision and ethics, leveraging students\u0026rsquo; demonstrated technical strengths, and implementing coordinated, resource-savvy reforms, Palestinian medical education can nurture a new generation of physicians equipped to harness AI for improved patient care, even amid ongoing regional challenges.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e Our study reveals that Palestinian medical students possess a solid foundation in the practical and cognitive dimensions of artificial intelligence, yet demonstrate notable deficits in ethical reasoning and strategic foresight. Demographic factors including gender, age, academic performance, and socioeconomic status as well as prior AI exposure, significantly influence readiness levels. The uniform readiness across universities suggests that curricular gaps are systemic rather than institution-specific, underscoring the need for a coordinated, national strategy. To prepare future physicians for an AI-augmented healthcare landscape, Palestinian medical curricula must evolve to integrate ethics, medico-legal discussions, and visionary case-based learning alongside hands-on technical training. Centralized development of standardized AI modules, supported by faculty development and inter-institutional collaboration, can ensure equitable and comprehensive AI literacy. Future research should employ longitudinal designs, objective skill assessments, and qualitative explorations to evaluate the impact of targeted interventions and to track readiness progression. By addressing both the strengths and gaps identified, stakeholders can equip Palestinian medical graduates to harness AI\u0026rsquo;s potential responsibly and effectively, ultimately enhancing patient care in a resource-constrained and conflict-affected setting.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e-Data availability\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThe data generated in this study are available upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-Acknowledgements:\u003c/strong\u003e We would like to express our sincere gratitude to the medical students who provided invaluable assistance in the data collection process for this study. In particular, we extend our appreciation to Haya Alshaikh, Ibrahim Al-Qunibi, Dana Sami, and Ansam Hirbawe for their dedication, commitment, and attention to detail. Their efforts significantly contributed to the quality and completeness of the data, and we are grateful for their support throughout this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-Funding:\u003c/strong\u003e There are no sources of funding to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-Author contributions:\u0026nbsp;\u003c/strong\u003eHamdah Hanifa, Alhareth M. Amro, and Salahaldeen Deeb contributed equally to the conception and design of the study, as well as to the writing and editing of both the initial and final versions of the manuscript. Ammir Abuzahra and Khaled Alhashlamon were responsible for data interpretation and contributed to the critical review of the manuscript. Tarek A. Owais performed the data analysis and provided methodological guidance. Yousef Abu Ayesh contributed to the methodology, and Nihad Assaf provided supervision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-Ethics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eAll procedures performed in this study involving human participants complied with the institutional and/or national research committee ethical standards and the 1964 Helsinki declaration and subsequent amendments or equivalent ethical standards. The study was designed and conducted in accordance with the ethical principles established by Al-Quds University. Therefore, ethical approval was obtained from the Institutional Review Board Committee, Faculty of Medicine, Al-Quds University.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eWritten informed consent was obtained from all the participants for the participation of this study and accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-Clinical Trial\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eNumber:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-Consent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-Competing interests:\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi Q, Qin Y. AI in medical education: medical student perception, curriculum recommendations and design suggestions. \u003cem\u003eBMC Med Educ\u003c/em\u003e. 2023;23(1):852. doi:10.1186/s12909-023-04700-8\u003c/li\u003e\n\u003cli\u003eDuan S, Liu C, Rong T, Zhao Y, Liu B. 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Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine. \u003cem\u003eBMC Med Educ\u003c/em\u003e. 2024;24(1):1-19. doi:10.1186/s12909-024-05465-4\u003c/li\u003e\n\u003cli\u003eBhaya AG. AI in times of conflict: How Palestinian entrepreneurs fight on. Published online 2025. https://www.trtworld.com/middle-east/ai-in-times-of-conflict-how-palestinian-entrepreneurs-fight-on-18268540\u003c/li\u003e\n\u003cli\u003eEjaz H, McGrath H, Wong BL, Guise A, Vercauteren T, Shapey J. Artificial intelligence and medical education: A global mixed-methods study of medical students\u0026rsquo; perspectives. \u003cem\u003eDigit Heal\u003c/em\u003e. 2022;8:20552076221089100. doi:10.1177/20552076221089099\u003c/li\u003e\n\u003cli\u003eLugito NPH, Cucunawangsih C, Suryadinata N, et al. Readiness, knowledge, and perception towards artificial intelligence of medical students at faculty of medicine, Pelita Harapan University, Indonesia: a cross sectional study. \u003cem\u003eBMC Med Educ\u003c/em\u003e. 2024;24(1):1044. doi:10.1186/s12909-024-06058-x\u003c/li\u003e\n\u003cli\u003eIsmail A, Ismail A, Alazar A, Saman M, Abu-Elqomboz A, Sharaf FK. E-Learning Medical Education in Gaza During COVID-19: Students\u0026rsquo; Experiences and Policy Recommendations. \u003cem\u003eJ Med Educ Curric Dev\u003c/em\u003e. 2023;10:23821205231164228. doi:10.1177/23821205231164228\u003c/li\u003e\n\u003cli\u003eHanifa H, Amro AM. Promoting equity in medical research: ensuring access to publishing opportunities for researchers in Syria and Palestine. \u003cem\u003eBMC Med Educ\u003c/em\u003e. 2024;24(1):1371. doi:10.1186/s12909-024-06375-1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial intelligence, MAIRS_MS, Cross sectional, Chat GPT, Medical students, Palestine","lastPublishedDoi":"10.21203/rs.3.rs-6623487/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6623487/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eArtificial intelligence (AI) enables computers to process data and solve problems via algorithms, with China at the forefront of medical AI applications like diagnostics. Medical students increasingly rely on AI tools (e.g., ChatGPT) for education, and ML advances predictive research.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study assessed AI readiness among 799 medical students from all universities in the West Bank, Palestine, that have a Faculty of Medicine, using the validated MAIRS-MS scale (22 items across 4 domains). Data collection combined electronic and paper questionnaires, ensuring high participation and reliability (α\u0026thinsp;=\u0026thinsp;0.87).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e Most participants were from Hebron University (66%) and represented all academic years. The majority (83%) were aware of AI in medicine, and 73% had prior experience with AI tools. The median total readiness score was 73 (IQR: 66\u0026ndash;84), with highest scores in ability (median: 27) and cognition (median: 26), and lower scores in vision and ethics (both median: 10). Males, older students, high-GPA achievers, and those from higher-income backgrounds had significantly higher readiness scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Prior AI experience and awareness were also strongly associated with increased readiness. No significant differences were observed across universities or academic years.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePalestinian medical students demonstrate moderate to high readiness for AI integration in medicine, particularly in technical and cognitive domains. However, notable gaps remain in ethical understanding and visionary thinking. Addressing these gaps requires national curricular reform focused on ethics, regulation, and strategic AI applications. Equitable access to AI education across socioeconomic and gender lines is essential to prepare future physicians for a digitally enhanced healthcare landscape.\u003c/p\u003e","manuscriptTitle":"Embracing Artificial Intelligence: Evaluating Technological Adaptability in Palestinian Medical Education","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 10:07:59","doi":"10.21203/rs.3.rs-6623487/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6dca3b5d-f0af-47c5-a4f8-e01cad5c1fbf","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-11T11:21:01+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T12:30:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-17 10:07:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6623487","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6623487","identity":"rs-6623487","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}