Assessing the level of readiness for digital transformation in medicine: Students of Ahvaz Jundishapur University of Medical Sciences for the use of artificial intelligence in health

Assessing the level of readiness for digital transformation in medicine: Students of Ahvaz Jundishapur University of Medical Sciences for the use of artificial intelligence in health | 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 Assessing the level of readiness for digital transformation in medicine: Students of Ahvaz Jundishapur University of Medical Sciences for the use of artificial intelligence in health Effat Abbasi Montazeri, Javad Zarei, Bahareh Ghavami Hoseinpour, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7313375/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Feb, 2026 Read the published version in BMC Medical Education → Version 1 posted 11 You are reading this latest preprint version Abstract Background Artificial intelligence (AI) is rapidly transforming healthcare by enhancing diagnostic accuracy, enabling personalized treatments, and improving patient outcomes. Medical students, as future healthcare providers and primary AI users, require adequate knowledge and readiness to integrate AI effectively in clinical practice. Despite growing global interest, little is known about the preparedness of medical students in Iran to adopt AI technologies. Methods A descriptive cross-sectional survey was conducted among 321 students from medicine, dentistry, and pharmacy programs at Ahvaz Jundishapur University of Medical Sciences during the 2024–2025 academic year. Data were collected via a validated 22-item AI readiness scale covering four domains: cognition, competency, vision, and ethics. Descriptive and inferential statistics, including one-sample t-tests and Wilcoxon signed-rank tests, were applied based on data distribution. Correlation analyses explored relationships among readiness components. Result Participants demonstrated moderate cognitive readiness (mean = 3.03), indicating an average theoretical understanding of AI. Competency in AI application scored significantly above average (mean = 3.44, p < 0.001), reflecting confidence in practical use, particularly with digital health tools. The vision toward AI in medicine was positive (mean = 3.31, p < 0.001), although varied among students. Ethical awareness scored highest (mean = 3.69, p < 0.001), indicating strong sensitivity to AI’s ethical challenges. Significant positive correlations were found among all domains (r = 0.44 to 0.73, p < 0.01), with the strongest between cognition and competency. Despite general optimism, gaps remain in technical knowledge and regulatory understanding. Conclusion Medical students in this cohort demonstrate encouraging readiness to engage with AI, particularly in practical and ethical domains; however, foundational knowledge and technical literacy need to be strengthened. The findings underscore the urgent need to integrate interdisciplinary AI education, hands-on training, and legal-ethical instruction into medical curricula. These initiatives are essential to prepare future healthcare professionals for effective and responsible AI integration, ultimately enhancing the quality of patient care. Artificial Intelligence Cognitive Readiness Technology Acceptance Student Attitudes Medical Education Introduction With its remarkable advancements, artificial intelligence (AI) has emerged as a transformative force in healthcare systems. AI has significant potential to enhance diagnostic accuracy, enable personalized treatment planning, and improve overall patient outcomes ( 1 ). As a result, the successful integration of AI into clinical settings is increasingly recognized as a key priority in modern healthcare systems. Medical students, as future physicians and primary end-users of AI tools, play a pivotal role in ensuring the success of this digital transformation. Numerous international studies suggest that students recognize the potential of AI in medicine ( 2 ). However, their practical knowledge and readiness to implement AI in clinical practice remain limited. For example, a study conducted in Germany found that although most students were familiar with general-purpose AI tools such as ChatGPT, their knowledge of more specialized clinical applications remained limited ( 3 ). Similar findings have been reported in China (Al-Gerafi et al., 2024) and Jordan (Rjoop et al., 2025), where students primarily acquire AI knowledge through non-academic sources. This highlights the urgent need for structured AI education in academic curricula ( 4 , 5 ). Despite its potential, the implementation of AI in healthcare faces challenges, including algorithmic opacity and complex operational frameworks ( 6 ), which may foster reluctance or resistance among healthcare professionals. Therefore, evaluating the perceptions, knowledge, and preparedness of future users, particularly medical students, is critical to ensuring successful and ethical adoption ( 7 ). A thorough understanding of AI enables students to critically evaluate AI-generated outputs and integrate them effectively into decision-making processes ( 8 ). This level of engagement directly influences their future professional attitudes and clinical judgment quality ( 9 , 10 ). Furthermore, consistent with established educational theories in medical training, evaluating learners' prior knowledge and readiness is a critical prerequisite before introducing new instructional content.This approach, particularly in the context of AI, aligns with constructivist principles and ensures that subsequent education is both relevant and effective ( 11 – 13 ). Several studies have emphasized the need to assess medical students' knowledge, attitudes, and behaviors regarding AI integration into healthcare ( 14 , 6 ). Therefore, equipping medical students with the necessary competencies to integrate AI into clinical and educational settings is essential not only to overcome existing barriers but also to fully realize AI's potential in improving patient care ( 15 ). Understanding students' readiness can inform curriculum development and educational policy, paving the way for more innovative and adaptive medical education systems ( 16 ). Although this topic is critically important, there has been limited research in Iran on medical students’ preparedness for adopting AI in clinical settings. Filling this gap can provide valuable insight into both the opportunities and challenges of AI implementation in medical practice. Objectives This study seeks to evaluate the preparedness of medical students to integrate and utilize artificial intelligence in both clinical practice and educational setting. Specifically, the study addresses the following research questions: What is the level of cognitive readiness among medical students for using AI in healthcare? What are students' competencies in applying AI tools within the medical field? How do students perceive the future role of AI in medicine? What is the extent of students’ understanding of the ethical considerations surrounding the use of AI in healthcare? Literature Review Scientific and Technological Landscape of Artificial Intelligence in Healthcare AI has rapidly transformed numerous sectors of healthcare, revolutionizing areas ranging from diagnostic imaging to personalized treatment planning ( 17 ). AI’s profound influence on healthcare is supported by its capability to process vast amounts of data, improve diagnostic accuracy, and streamline patient care workflows ( 17 – 18 ). The integration of AI in medicine is driven by sophisticated algorithms, increasing access to relevant patient data, and the advent of advanced machine learning techniques that promise to transform traditional clinical practicesMoreover, emerging technologies such as natural language processing, computer vision, and predictive analytics have significantly boosted the potential of AI applications in healthcare, making it a leading investigative topic among healthcare professionals and researchers ( 18 – 19 ).Recognizing the transformative potential of AI, medical education programs should prepare future healthcare professionals to skillfully integrate AI tools into their clinical decision-making processe ( 20 – 21 ). The Growing Role of AI in Healthcare AI is increasingly seen as a key factor in the evolution of medical education, with the potential to revolutionize healthcare systems.( 22 ) This includes the use of AI in various applications such as robot-assisted surgical training, intelligent assessment feedback systems, and smart virtual simulation systems( 23 ). As AI continues to advance, medical professionals must be trained to effectively use this technology to improve the cost, quality, and accessibility of healthcare.( 24 ) Integrating AI into medical curriculum can equip students with the skills and knowledge necessary to foster a patient-centered, digitally advanced future in healthcare ( 25 ). Importance of AI Readiness in Medical Students Medical students, as future clinicians and healthcare leaders, are key stakeholders in the clinical implementation of AI technologies. ( 25 , 26 ) Assessing their readiness to embrace AI is crucial for successful adoption and its value in the medical field ( 27 – 28 ). Evaluating medical students’ AI readiness involves assessing their understanding, attitudes, and perceived significance of AI in medicine ( 26 ). It is also important to address ethical considerations, health equity, and data security concerns related to AI ( 29 ). Attitudes and Perceptions Medical students generally hold positive attitudes toward AI in healthcare, with many recognizing its potential to improve medical outcomes ( 30 – 31 ). A survey of medical students in Kancheepuram District, Tamil Nadu, revealed that 85% of participants were aware of AI, and 98% believed it could improve healthcare ( 32 ). Most students (71%) felt that AI teaching would benefit their careers, and 69.44% agreed that all students should receive AI training ( 33 ). Despite these positive attitudes, some students express concerns about the impact of AI on employment prospects and ethical issues ( 34 – 35 ). For example, a study showed that 32.55% of students were less likely to consider a career in radiology due to the advancement of AI ( 35 ). Methods Study design This was a descriptive cross-sectional study with a survey-based approach, conducted during the second academic semester of 2024–2025 at Ahvaz Jundishapur University of Medical Sciences. Setting and participants The study population included students enrolled in general medicine, dentistry, and pharmacy programs. Using a purposive sampling approach, 321 students were recruited. The inclusion criteria were willingness to participate in the study and prior exposure and familiarity with the study topic. Exclusion criteria were incomplete questionnaire responses and withdrawal of consent at any stage. Data collection instruments The data collection instrument was a standardized "AI Readiness" scale, consisting of 22 questions across four main dimensions: cognitive readiness, competency, perspective, and ethics. Questions were scored on a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree). The reliability of the scale was confirmed by Karaca (2021) with a Cronbach's alpha coefficient of 0.87 ( 36 ). Its reliability and validity have also been established in local studies, with Reza Zadeh et al. (2023) reporting a Cronbach's alpha of 0.94 and content validity indices of 0.92 and 0.75 respectively ( 37 ). Data collection procedure Data were collected both in-person and online via Google Forms. Online questionnaires were distributed through student Telegram groups. All participants provided written informed consent and took part in the study voluntarily. Data Analysis After verifying data adequacy and confirming internal consistency of the instrument using Cronbach’s alpha, the dataset was subjected to descriptive and inferential statistical analysis. Descriptively, demographic characteristics such as gender and prior familiarity with artificial intelligence were summarized using frequencies, percentages, means, and standard deviations. Central tendency and dispersion indices were also calculated for each readiness domain, and visualized through tables and graphs to enhance interpretability. Inferentially, both parametric (one-sample t-test, Pearson correlation) and non-parametric tests (Wilcoxon signed-rank test, Spearman correlation) were employed to test the study hypotheses and explore associations between variables. The choice of statistical procedures was guided by the results of the Kolmogorov–Smirnov test, which indicated that while some variables met the assumption of normality, others did not. Accordingly, appropriate methods were selected based on the distributional properties of the data. All statistical analyses were performed using SPSS (version 26), with the results presented in tabular format. Results This section presents the statistical findings derived from the analysis of the collected data, structured around the main components of AI readiness. Both descriptive and inferential statistics are reported to provide a comprehensive understanding of students' preparedness for digital transformation in healthcare. Participant Demographics A total of 321 students participated in the study, of whom 55.6% were male and 44.5% were female (Table 1 ). Table 1 Demographic Characteristics of the Participants Gender Frequency Percent Male 178 55.6% Female 143 44.5% Cognitive Readiness Descriptive analysis for the Cognitive Readiness for AI Application component revealed a mean score of 3.033 (SD = 0.754), with a median of 3.125 and scores ranging from 1.00 to 5.00. The skewness value of − 0.009 indicated a nearly symmetric distribution. Although the Kolmogorov–Smirnov test indicated a statistically significant result (K–S = 0.030; p < 0.05), the distribution was sufficiently close to normal (Table 2 ). Given this near symmetry and the large sample size, parametric tests were considered appropriate for the inferential analysis . Table 2 Descriptive Statistics for the “Cognitive Readiness” Component Mean SD Median Min Max Skewness K-S ‌ K-S Sig. 3.033 0.754 3.125 1.00 5.00 -0.009 0.030 A one-sample t-test was conducted to compare the cognitive readiness scores against the test value of 3. The result (t(320) = 0.782, p = 0.435) indicated no statistically significant difference from the neutral benchmark (Table 3 ). This indicates that students' conceptual and theoretical understanding of artificial intelligence is generally balanced, reflecting a moderate level of confidence and no significant gaps in their cognitive readiness to engage with AI in medical contexts. Table 3 One-Sample t-Test Results for the “Cognitive Readiness” Component t df Sig. (2-tailed) Mean Diff 95% CI Lower 95% CI Upper 0.782 320 0.435 0.03291 -0.0499 0.1158 Competency in AI Application Descriptive analysis for the 'Competency in AI Application' dimension revealed a mean score of 3.440 (SD = 0.818), with a median of 3.625 and a score range spanning from 1.00 to 5.00. The negative skewness value (–0.784) indicates a slight leftward skew in the distribution, suggesting that more participants rated themselves above the neutral midpoint. The Kolmogorov–Smirnov test for normality yielded a statistic of 0.000, indicating a significant deviation from normal distribution (p < 0.01).This violation of normality assumptions justifies the use of non-parametric tests, such as the Wilcoxon signed-rank test, for further inferential analysis (Table 4 ). Table 4 Descriptive Statistics for the “Competency in AI Application” Component Mean SD Median Min Max Skewness K-S K-S Sig. 3.440 0.818 3.625 1.00 5.00 -0.784 0.000 To evaluate students’ perceived competency in applying artificial intelligence, the Wilcoxon signed-rank test was employed to compare observed scores against the neutral benchmark value of 3. Out of 305 responses, 232 were greater than 3 and 70 were below. The test produced a Z value of 11.20 with a significance level of p = 0.000, confirming a statistically significant deviation from the midpoint (Table 5 ). This finding indicates that participants rated their AI competency significantly above average. The positive shift in scores reflects a general sense of confidence among students in using AI tools within academic or clinical settings, despite potential variation in actual skill levels. Table 5 Wilcoxon Signed-Rank Test Results Observation Frequency Sum of Ranks Wilcoxon Statistic (Z) Significance Level (p-value) Greater than 3 232 40606.5 11.20 0.000 Less than 3 70 10884.5 Total 305 Vision Toward AI Application Descriptive statistics for the 'Vision Toward AI Application' component revealed a mean score of 3.313 (SD = 0.923), with a median of 3.333 and scores ranging from 1.00 to 5.00. The skewness value of − 0.562 indicates a slight negative skew, suggesting that more students rated this dimension above the neutral midpoint. The Kolmogorov–Smirnov test yielded a significance level of p = 0.000, indicating that the data distribution significantly deviates from normality (Table 6 ). As a result, non-parametric tests were employed for further analysis. Table 6 Descriptive Statistics for the “Vision Toward in AI Application” Component Mean SD Median Min Max Skewness K-S K-S Sig. 3/313 0.923 3.333 1.00 5.00 -0.562 0.000 The Wilcoxon signed-rank test was conducted to compare the median vision scores against the test value of 3. Out of 266 participants, 184 had scores above 3 and 82 had scores below. The test produced a Z value of 14.19 with a p-value less than 0.001, indicating a statistically significant positive deviation (Table 7 ). These findings suggest that students, on average, hold a significantly positive outlook toward the application of artificial intelligence in medicine. However, the wide range of scores (from 1 to 5) may reflect substantial variation in students’ experiences, expectations, or levels of awareness regarding AI in medical contexts. Table 7 Wilcoxon Signed-Rank Test Results Observation Frequency Sum of Ranks Wilcoxon Statistic (Z) p-value Greater than 3 184 35,558.5 14.19 0.000 Less than 3 82 14,582.5 Total 266 Ethical Awareness in AI Usage Descriptive statistics for the Ethical Readiness for AI Application component revealed a mean score of 3.690 (SD = 0.832), with a median of 4.00 and scores ranging from 1.00 to 5.00.The skewness value of − 0.789 reflects a moderate left skew, suggesting that most students rated themselves relatively high in terms of ethical awareness.The Kolmogorov–Smirnov test confirmed a significant deviation from normal distribution (p < 0.001), supporting the use of non-parametric tests for inferential analysis (Table 8 ). Table 8 Descriptive Statistics for the “Ethical Awareness in AI Usage” Component Mean SD Median Min Max Skewness K-S K-S Sig. 3/690 0.832 4.00 1.00 5.00 -0.789 0.000 A Wilcoxon signed-rank test was conducted to determine whether students' ethical readiness scores significantly exceeded the benchmark value of 3. Among the 266 participants, 184 reported scores above 3 and 82 below 3, resulting in a test statistic of Z = 12.04 (p < 0.001) (Table 9 ). This indicates a statistically significant positive deviation, meaning that students, on average, possess a strong ethical orientation regarding the use of AI in medical practice. This relatively high level of ethical sensitivity may reflect an increasing awareness of digital ethics within the medical education environment. However, the notable standard deviation implies variability in individual levels of ethical understanding suggesting that while many students are ethically conscious, others may still possess inconsistent or superficial ethical insight. Table 9 Wilcoxon Signed-Rank Test Results Observation Frequency Sum of Ranks Wilcoxon Statistic (Z) p-value Greater than 3 184 32,886 12.04 0.000 Less than 3 82 11,751 Total 266 Correlations The following two tables present the bivariate correlations among the four principal constructs of the questionnaire. (Table 10 ) displays the Pearson correlation coefficients, whereas (Table 11 ) reports the Spearman’s rank-order correlation coefficients as a non-parametric alternative. Table 10 Pearson correlation matrix among core questionnaire components; *p < 0.01 1 Variable Cognition Competency Vision Ethics Cognition 1.000 0.729** 0.667** 0.440** Competency 0.729** 1.000 0.707** 0.606** Vision 0.667** 0.707** 1.000 0.563** Ethics 0.440** 0.606** 0.563** 1.000 The results of Pearson correlation analysis indicated a statistically significant and positive relationship among all four components of AI readiness (p < 0.01). The strongest correlation was observed between Cognition and Competency (r = 0.729), suggesting that students with higher conceptual understanding of AI are more likely to feel competent in using it. Moreover, Competency showed a strong association with both V (r = 0.707) and Ethics (r = 0.606), highlighting the central role of practical skills in shaping positive perceptions and ethical awareness. The weakest correlation was found between Cognition and Ethics (r = 0.440), implying that conceptual knowledge alone may not strongly predict ethical sensitivity, and that experiential or value-based training may be required to bridge this gap. Table 11 Spearman correlation matrix among core questionnaire components 2 Variable Cognition Competency Vision Ethics Cognition 1.000 0.660** 0.586** 0.349** Competency 0.660** 1.000 0.629** 0.512** Vision 0.586** 0.629** 1.000 0.482** Ethics 0.349** 0.512** 0.482** 1.000 The Spearman correlation analysis similarly revealed statistically significant and positive relationships among all core components of the questionnaire (p < 0.01). The strongest association was found between Cognition and Competency (rₛ = 0.660), underscoring the foundational role of conceptual understanding in shaping functional capabilities. Competency also showed strong correlations with both Vision (rₛ = 0.629) and Ethics (rₛ = 0.512), suggesting that perceived ability plays a pivotal role in shaping values and perspectives. The weakest, albeit significant, correlation was observed between Cognition and Ethics (rₛ = 0.349), indicating a potential disconnect between theoretical knowledge and ethical considerations in AI. These findings support the internal coherence of the questionnaire dimensions while underscoring the importance of integrative education in connecting conceptual and ethical domains. Discussion This study provides important insights into the cognitive readiness, practical competence, Vision, and ethical awareness of medical students regarding the application of AI in healthcare. The findings reveal notable strengths as well as critical gaps, offering valuable insights for curriculum development and the design of future training programs. The findings of this study reveal that medical students possess only a moderate level of familiarity with fundamental AI concepts, and they demonstrate notable weaknesses in understanding more specialized processes and practical applications of AI in the healthcare domain. This limited and unstructured familiarity indicates an insufficient readiness to engage effectively in professional environments increasingly shaped by AI technologies. Contributing factors may include the lack of formal instruction, inadequate interdisciplinary content, and a persistent gap between foundational sciences and applied technologies in current medical education systems. These findings are consistent with prior research by Hamdani et al. (2023) and Lee & Chin (2023), which emphasized students’ limited knowledge and cautious attitudes toward AI integration ( 38 , 39 ). In contrast, studies by Giovanner (2022) and Bisdas (2021) report more favorable perceptions and broader acceptance of AI, likely influenced by differences in educational approaches, resource availability, and cultural contexts ( 40 , 41 ).The significance of these findings lies in highlighting a systemic shortfall: current medical education frameworks have yet to integrate AI as a central, future-oriented component. This inadequacy not only hinders the effective implementation of emerging technologies in healthcare delivery, but also risks widening the gap between academic instruction and clinical practice. Therefore, the development and integration of interdisciplinary curricula, hands-on workshops, and dedicated academic modules on health-related technologies must be recognized as a strategic educational priority. Regarding students' AI-related competencies, the findings suggest that their proficiency in utilizing AI tools particularly in medical education, healthcare services, and research is above average. This relative strength is most evident in routine engagement with digital health applications and purposeful use of online resources. However, their analytical skills and clinical decision-making abilities in applying AI remain underdeveloped. These findings suggest that students’ competencies are largely practice-based and are not significantly influenced by demographic factors such as age or years of study. This is in line with studies by Lee & Chin (2023)and Sabet et al. (2023), which found that hands-on experience with AI strongly correlates with enhanced user competence and conceptual understanding ( 39 , 42 ). Similar trends were observed in studies by Ali & Moghari (2024) and Laupichler et al. (2024), indicating that while students demonstrate an acceptable level of AI literacy in practical domains, their understanding of algorithmic errors and technical intricacies remains limited ( 43 , 44 ). This alignment suggests that existing training programs tend to emphasize applied and general uses of AI, without adequately addressing analytical and decision-making competencies. Conversely, the study by Beigi et al. (2022) reported lower practical skills, possibly reflecting the limited exposure to research opportunities among their participants ( 45 ). The discrepancy with Sabavierpandian et al. (2024) in Zambia may be attributed to infrastructural differences and restricted access to AI education in low-resource settings ( 46 ). Students’ attitudes toward the application of AI in medicine were generally positive and optimistic. This favorable outlook is particularly evident in their ability to anticipate the potential benefits and risks of AI, although less so in their understanding of its technical limitations. These results echo those of Beigi et al. (2022), where most health students regarded AI as a beneficial tool for the future of medicine ( 45 ). However, this optimism appears to be shaped more by general exposure to digital media than by practical or technical experience with AI systems. This trend aligns with Santos (2019), who also found strong support among students for AI integration in education and healthcare delivery ( 2 ). The data further suggest that even minimal exposure to AI-related technologies can contribute to a more favorable attitude toward their use. This observation aligns with the findings of Sabet et al. (2023) and Mirzaei & Askouei (2022) and, which showed that participation in structured training programs or hands-on experiences significantly enhances not only students’ knowledge but also their attitudes toward AI ( 42 , 47 ).In terms of ethical considerations, students demonstrated relatively high awareness of AI ethics, particularly concerning the legal and moral use of health data. This finding is consistent with prior studies by Sabet et al. (2023) and Beigi et al. (2022) and, which reported improvements in students' ethical understanding following targeted education ( 42 , 45 ). Nevertheless, although overall ethical awareness was adequate, students scored lower on items related to regulatory compliance and national legal standards. This suggests a gap between general ethical values such as data confidentiality and fairness and concrete knowledge of local legal frameworks. Addressing this gap requires integrating legal literacy and policy training into medical curricula to ensure that future professionals can effectively apply ethical principles within real-world digital health ecosystems. Limitations and Recommendations This study had certain limitations that should be acknowledged. The cross-sectional design limits causal inference, and the findings are based on self-reported data, which may be subject to social desirability or recall biases. Moreover, the generalizability of the results may be limited due to the specific demographic and institutional contexts of the participants. In light of the findings, there is a pressing need to develop structured interdisciplinary educational programs, practical workshops, and dedicated academic modules focused on health technologies and artificial intelligence within the medical education system. Emphasizing hands-on experience and targeted training can help bridge the existing gap between theoretical knowledge and practical competence, ultimately enabling more effective integration of emerging technologies in healthcare delivery. Conclusion The findings of this study, encompassing four pivotal constructs cognitive readiness, competency, outlook, and ethics in the application of artificial intelligence indicate that although students exhibit foundational preparedness in certain areas, their overall readiness to effectively implement AI technologies within clinical, educational, and research domains remains suboptimal. Notable strengths include practical skills and a generally favorable vision towards AI applications. Nevertheless, significant gaps persist in the comprehensive understanding of specialized AI concepts, analytical capabilities, and awareness of associated legal and regulatory frameworks, underscoring the urgent eed for well-structured and focused educational programs. While students display familiarity with basic concepts such as statistics and data management, this level of cognitive understanding falls short of enabling them to grasp the intricate workings of AI algorithms and predictive models. Competency-wise, although some proficiency in utilizing relevant software and integrating AI into clinical practice exists, limitations remain in selecting appropriate tools to address complex healthcare challenges. The prevailing outlook is positive and future-oriented; however, it is largely influenced by general perceptions rather than grounded practical knowledge. Ethically, a foundational commitment is evident, but the ability to discern and manage nuanced ethical issues arising from emerging AI technologies is insufficient. Consequently, it is imperative that educational and research institutions implement comprehensive, multidimensional training initiatives aimed at advancing specialized knowledge, analytical skills, and ethical-legal literacy concerning AI. Such measures are essential to safely and effectively harness the full potential of AI innovations across the healthcare, education, and research sectors. Abbreviations Artificial Intelligent: AI Declarations Ethics approval and consent to participate This study was designed and conducted in accordance with the ethical principles of the Declaration of Helsinki. Ethical approval was obtained from the Research Ethics Committee of Ahvaz Jundishapur University of Medical Sciences. (Ethics Code: IR.AJUMS.REC.1403.432) prior to the commencement of the study. Consent for publication Not applicable Funding Not applicable Author Contribution B.GH.H. and E.A.M. conceived and designed the study. E.A.M., B.GH.H., and J.Z. contributed to the development of the methodology. J.Z., A.B., and B.GH.H. performed the data analysis, while A.B. was responsible for data collection. B.GH.H. drafted the original manuscript. All authors contributed to the interpretation of the results, critically revised the manuscript for important intellectual content, and approved the final version for publication. Supervision was provided by E.A.M.- Conceptualization: E.A.M., B.GH.H.- Methodology: E.A.M., B.GH.H., J.Z.- Formal analysis: J.Z., A.B., B.GH.H.- Investigation (data collection): A.B.- Writing – original draft: B.GH.H.- Writing – review & editing: All authors- Data interpretation: All authors- Supervision: E.A.M.- Final approval of the manuscript: All authors Acknowledgements Not applicable Availability of data and materials The datasets generated and /or analyzed during the current study are available from the corresponding author on reasonable request. Due to privacy and ethical restrictions related to participant confidentiality, the data are not publicly available. References Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, Aldairem A, Alrashed M, Bin Saleh K, Badreldin HA, Al Yami MS. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. 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Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Syria: a cross-sectional online survey. Front Artif Intell. 2022;5. https://doi.org/10.3389/frai.2022.1011524 Doumat G, Daher D, Ghanem NN, Khater B. Knowledge and attitudes of medical students in Lebanon toward artificial intelligence: a national survey study. Front Artif Intell. 2022;5:1015418. https://doi.org/10.3389/frai.2022.1015418 Talati D. AI in healthcare domain. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online). 2023 Dec 12;2(3):256-62. https://doi.org/10.60087/jklst.vol2.n3.p253 Aldergham M, Alfouri A, Madat RA. Artificial Intelligence in Medicine. SEEJPH [Internet]. 2024 Oct. 9 [cited 2025 Aug. 5];:774-90. Available from: https://www.seejph.com/index.php/seejph/article/view/1561 Wang F, Casalino LP, Khullar D. Deep learning in medicine—promise, progress, and challenges. JAMA internal medicine. 2019 Mar 1;179(3):293-4. https://doi.org/10.1001/jamainternmed.2018.7117 Naqvi WM, Sundus H, Mishra G, Muthukrishnan R, Kandakurti PK. AI in medical education curriculum: the future of healthcare learning. European journal of therapeutics. 2024 Jan 30;30(2):e23-5. https://doi.org/10.58600/eurjther1995 Jamil B. Transforming Medical and Dental Curriculum in the era of Artificial Intelligence (AI). Journal of Gandhara Medical and Dental Science. 2024 Sep 30;11(4):1-2. https://doi.org/10.37762/jgmds.11-4.625 Pregowska A, Perkins M. Artificial intelligence in medical education: Typologies and ethical approaches. Ethics & Bioethics. 2024;14(1-2):96-113. Chen F, Xia J, Yu X, Zhuge J. Landscape and Trends in the Application of Artificial Intelligence in Medical Education. In2023 International Conference on Intelligent Education and Intelligent Research (IEIR) 2023 Nov 5 (pp. 1-6). IEEE. https://doi.org/10.1109/ieir59294.2023.10391248 Paranjape K, Schinkel M, Panday RN, Car J, Nanayakkara P. Introducing artificial intelligence training in medical education. JMIR medical education. 2019 Dec 3;5(2):e16048. https://doi.org/10.2196/16048 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. Digital Health. 2022 May;8:20552076221089099. https://doi.org/10.1177/20552076221089099 Xuan PY, Fahumida F, Ismath M, Al Nazir Hussain MI, Jayathilake NT, Khobragade S, Soe HH, Moe S, Htay N, Nu M. Readiness Towards Artificial Intelligence Among Undergraduate Medical Students in Malaysia. Education in Medicine Journal. 2023 Jun 1;15(2). https://doi.org/10.21315/eimj2023.15.2.4 Dhurandhar D, Dhamande M, C S, Bhadoria P, Chandrakar T, Agrawal J. Exploring Medical Artificial Intelligence Readiness Among Future Physicians: Insights From a Medical College in Central India. Cureus. 2025 Jan 3;17(1):e76835. doi: 10.7759/cureus.76835. https://doi.org/10.7759/cureus.76835 Reverón RR. Artificial intelligence in current undergraduate medical education. Gaceta Medica De Caracas. 2024 Jun 5;132(2). https://doi.org/10.47307/gmc.2024.132.2.24 Mikić D, Glomazić H, Mikić A. Medical students’ perception of the role of artificial intelligence in healthcare. Medicinski pregled. 2023;76(9-10):269-74. https://doi.org/10.2298/mpns2310269m AlZaabi A, AlMaskari S, AalAbdulsalam A. Are physicians and medical students ready for artificial intelligence applications in healthcare?. Digital health. 2023 Jan;9:20552076231152167. https://doi.org/10.1177/20552076231152167 Parvathavarthine CR, Phani Krishna MK, Janaki CS, Sophia M, Chandar SN. Awareness about the future of artificial intelligence in healthcare among medical students in Kancheepuram District, Tamil Nadu. International journal of health sciences. 2022;6(S8):5697-705. https://doi.org/10.53730/ijhs.v6ns8.13571 Tung AY, Dong LW. Malaysian medical students’ attitudes and readiness toward AI (artificial intelligence): a cross-sectional study. Journal of Medical Education and Curricular Development. 2023 Sep;10:23821205231201164. https://doi.org/10.1177/23821205231201164 Al Hadithy ZA, Al Lawati A, Al-Zadjali R, Al Sinawi H. Knowledge, attitudes, and perceptions of artificial intelligence in healthcare among medical students at Sultan Qaboos University. Cureus. 2023 Sep 8;15(9). https://doi.org/10.7759/cureus.44887 Mehta N, Harish V, Bilimoria K, Morgado F, Ginsburg S, Law M, Das S. Knowledge of and attitudes on artificial intelligence in healthcare: a provincial survey study of medical students. Medrxiv. 2021 Jan 15:2021-01. (4). https://doi.org/10.15694/mep.2021.000075.1 Hassankhani A, Amoukhteh M, Valizadeh P, Jannatdoust P, Sabeghi P, Gholamrezanezhad A. Radiology as a specialty in the era of artificial intelligence: a systematic review and meta-analysis on medical students, radiology trainees, and radiologists. Academic Radiology. 2024 Jan 1;31(1):306-21. https://doi.org/10.1016/j.acra.2023.05.024 Karaca O, Çalışkan SA, Demir K. Medical artificial intelligence readiness scale for medical students (MAIRS-MS)–development, validity and reliability study. BMC medical education. 2021 Dec;21:1-9. https://doi.org/10.1186/s12909-021-02546-6 Rezazadeh H, Ahmadipour H, Salajegheh M. Psychometric evaluation of Persian version of medical artificial intelligence readiness scale for medical students. BMC Medical Education. 2023 Jul 24;23(1):527. https://doi.org/10.1186/s12909-023-04516-6 Hamedani Z, Moradi M, Kalroozi F, Manafi Anari A, Jalalifar E, Ansari A, et al. Evaluation of acceptance, attitude, and knowledge towards artificial intelligence and its application from the point of view of physicians and nurses: a provincial survey study in Iran: a cross‐sectional descriptive‐analytical study. Health science reports. 2023;6(9):e1543. https://doi.org/10.1002/hsr2.1543 Li Q, Qin Y. AI in medical education: medical student perception, curriculum recommendations and design suggestions. BMC Medical Education. 2023;23(1):852. https://doi.org/10.1186/s12909-023-04700-8 Civaner MM, Uncu Y, Bulut F, Chalil EG, Tatli A. Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Medical Education. 2022;22(1):772. https://doi.org/10.1186/s12909-022-03852-3 Bisdas S, Topriceanu C-C, Zakrzewska Z, Irimia A-V, Shakallis L, Subhash J, et al. Artificial intelligence in medicine: a multinational multi-center survey on the medical and dental students' perception. Frontiers in Public Health. 2021;9:795284. https://doi.org/10.3389/fpubh.2021.795284 Sabet B, Khani H, Namaki A, Habibi A, Rajabzadeh S, Shafiekhani S. Evaluation of artificial intelligence fall school program at Smart University of Medical Sciences. Research and Development in Medical Education. 2023;12(1):23-. https://doi.org/10.34172/rdme.2023.33142 Ali MA, Mughari S. Effect of AI literacy on online information search competencies among medical students in Pakistan. Information Development. 2025:02666669241299765. https://doi.org/10.1177/02666669241299765 Laupichler MC, Aster A, Meyerheim M, Raupach T, Mergen M. Medical students' AI literacy and attitudes towards AI: a cross-sectional two-center study using pre-validated assessment instruments. BMC Med Educ. 2024 Apr 10;24(1):401. doi: 10.1186/s12909-024-05400-7. https://doi.org/10.1186/s12909-024-05400-7 Mousavi Baigi SF, Sarbaz M, Ghaddaripouri K, Ghaddaripouri M, Mousavi AS, Kimiafar K. Attitudes, knowledge, and skills towards artificial intelligence among healthcare students: A systematic review. Health science reports. 2023;6(3):e1138. https://doi.org/10.1002/hsr2.1138 Subaveerapandiyan A, Mvula D, Ahmad N, Taj A, Ahmed MG. Assessing AI literacy and attitudes among medical students: implications for integration into healthcare practice. J Health Organ Manag 2024; doi.org/10.1108/JHOM-04-2024-0154. https://doi.org/10.1108/jhom-04-2024-0154 Mirzaeian VR, Oskoui K. Investigating Iranian EFL student teachers’ attitude toward the implementation of machine translation as an ICALL tool. Journal of English Language Teaching and Learning. 2022;14(30):165-79. https://doi.org/10.22034/elt.2022.52038.2496 Footnotes All correlation coefficients were significant at the 0.01 level, with p-values equal to 0.000 All coefficients are statistically significant at the 0.01 level (two-tailed) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Feb, 2026 Read the published version in BMC Medical Education → Version 1 posted Editorial decision: Revision requested 08 Sep, 2025 Reviews received at journal 02 Sep, 2025 Reviews received at journal 28 Aug, 2025 Reviews received at journal 27 Aug, 2025 Reviewers agreed at journal 26 Aug, 2025 Reviewers agreed at journal 26 Aug, 2025 Reviewers agreed at journal 26 Aug, 2025 Reviewers invited by journal 26 Aug, 2025 Editor assigned by journal 18 Aug, 2025 Submission checks completed at journal 14 Aug, 2025 First submitted to journal 13 Aug, 2025 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. 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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-7313375","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":507973102,"identity":"4a86f05c-e2dc-40f2-89b0-365848e5690c","order_by":0,"name":"Effat Abbasi Montazeri","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Effat","middleName":"Abbasi","lastName":"Montazeri","suffix":""},{"id":507973104,"identity":"1996fafb-5752-4f78-9390-a0f9b438bfaf","order_by":1,"name":"Javad Zarei","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Javad","middleName":"","lastName":"Zarei","suffix":""},{"id":507973106,"identity":"7b5a37a4-d78c-4d0c-b7ad-6e9f57f5ad00","order_by":2,"name":"Bahareh Ghavami Hoseinpour","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYBACewYeMJ0AJhP/2QBJxsYD+LQYNiBrSWBLA2lpwKvF4ACyFga2w2AKvy3tZw9+rqiozeOfkfx0wwOe83Zr2w8DbamxicalxZ4nL1nyzJnjxRI30sxuJEjcTt52JhGo5VhabgNOv+QYSDa2HUtsOHMAqMXgdrLZAaAWxobDOLUYnH9j/LPx37HE+WeOf7uRkHAu2ez8QwJabuSYSTY21CRuON4DtOXAATuzGwRsMZzxxsyy4diBxI3He8qAipMTzG4AbUnA4xd7/hzjmw01dYnzDrNvu/mzwc7e7Hz6wwcfamxwaoGCw3BWIlhlAn7lIFCHsJaw4lEwCkbBKBhpAAAwr3EeVsBIGgAAAABJRU5ErkJggg==","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Bahareh","middleName":"Ghavami","lastName":"Hoseinpour","suffix":""},{"id":507973107,"identity":"51a4eb53-5ba5-462a-b570-773e56ca40bd","order_by":3,"name":"Amir Bahadori","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Amir","middleName":"","lastName":"Bahadori","suffix":""}],"badges":[],"createdAt":"2025-08-06 23:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7313375/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7313375/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12909-026-08865-w","type":"published","date":"2026-02-23T15:59:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":103765639,"identity":"e6d0698d-5b3f-4655-a198-abc1ddc5c126","added_by":"auto","created_at":"2026-03-02 16:06:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1113543,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7313375/v1/bd8fd180-67e1-49dd-909a-774c6fa763ce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the level of readiness for digital transformation in medicine: Students of Ahvaz Jundishapur University of Medical Sciences for the use of artificial intelligence in health","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith its remarkable advancements, artificial intelligence (AI) has emerged as a transformative force in healthcare systems. AI has significant potential to enhance diagnostic accuracy, enable personalized treatment planning, and improve overall patient outcomes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). As a result, the successful integration of AI into clinical settings is increasingly recognized as a key priority in modern healthcare systems. Medical students, as future physicians and primary end-users of AI tools, play a pivotal role in ensuring the success of this digital transformation. Numerous international studies suggest that students recognize the potential of AI in medicine (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). However, their practical knowledge and readiness to implement AI in clinical practice remain limited. For example, a study conducted in Germany found that although most students were familiar with general-purpose AI tools such as ChatGPT, their knowledge of more specialized clinical applications remained limited (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Similar findings have been reported in China (Al-Gerafi et al., 2024) and Jordan (Rjoop et al., 2025), where students primarily acquire AI knowledge through non-academic sources. This highlights the urgent need for structured AI education in academic curricula (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Despite its potential, the implementation of AI in healthcare faces challenges, including algorithmic opacity and complex operational frameworks (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), which may foster reluctance or resistance among healthcare professionals. Therefore, evaluating the perceptions, knowledge, and preparedness of future users, particularly medical students, is critical to ensuring successful and ethical adoption (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). A thorough understanding of AI enables students to critically evaluate AI-generated outputs and integrate them effectively into decision-making processes (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This level of engagement directly influences their future professional attitudes and clinical judgment quality (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Furthermore, consistent with established educational theories in medical training, evaluating learners' prior knowledge and readiness is a critical prerequisite before introducing new instructional content.This approach, particularly in the context of AI, aligns with constructivist principles and ensures that subsequent education is both relevant and effective (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Several studies have emphasized the need to assess medical students' knowledge, attitudes, and behaviors regarding AI integration into healthcare (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Therefore, equipping medical students with the necessary competencies to integrate AI into clinical and educational settings is essential not only to overcome existing barriers but also to fully realize AI's potential in improving patient care (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnderstanding students' readiness can inform curriculum development and educational policy, paving the way for more innovative and adaptive medical education systems (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Although this topic is critically important, there has been limited research in Iran on medical students\u0026rsquo; preparedness for adopting AI in clinical settings. Filling this gap can provide valuable insight into both the opportunities and challenges of AI implementation in medical practice.\u003c/p\u003e\n\u003ch3\u003eObjectives\u003c/h3\u003e\n\u003cp\u003eThis study seeks to evaluate the preparedness of medical students to integrate and utilize artificial intelligence in both clinical practice and educational setting. Specifically, the study addresses the following research questions:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat is the level of cognitive readiness among medical students for using AI in healthcare?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat are students' competencies in applying AI tools within the medical field?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHow do students perceive the future role of AI in medicine?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat is the extent of students’ understanding of the ethical considerations surrounding the use of AI in healthcare?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e"},{"header":"Literature Review","content":"\u003ch2\u003eScientific and Technological Landscape of Artificial Intelligence in Healthcare\u003c/h2\u003e\u003cp\u003eAI has rapidly transformed numerous sectors of healthcare, revolutionizing areas ranging from diagnostic imaging to personalized treatment planning (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). AI’s profound influence on healthcare is supported by its capability to process vast amounts of data, improve diagnostic accuracy, and streamline patient care workflows (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e–\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The integration of AI in medicine is driven by sophisticated algorithms, increasing access to relevant patient data, and the advent of advanced machine learning techniques that promise to transform traditional clinical practicesMoreover, emerging technologies such as natural language processing, computer vision, and predictive analytics have significantly boosted the potential of AI applications in healthcare, making it a leading investigative topic among healthcare professionals and researchers (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e–\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).Recognizing the transformative potential of AI, medical education programs should prepare future healthcare professionals to skillfully integrate AI tools into their clinical decision-making processe (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e–\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003ch3\u003eThe Growing Role of AI in Healthcare\u003c/h3\u003e\u003cp\u003eAI is increasingly seen as a key factor in the evolution of medical education, with the potential to revolutionize healthcare systems.(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) This includes the use of AI in various applications such as robot-assisted surgical training, intelligent assessment feedback systems, and smart virtual simulation systems(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). As AI continues to advance, medical professionals must be trained to effectively use this technology to improve the cost, quality, and accessibility of healthcare.(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) Integrating AI into medical curriculum can equip students with the skills and knowledge necessary to foster a patient-centered, digitally advanced future in healthcare (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003ch3\u003eImportance of AI Readiness in Medical Students\u003c/h3\u003e\u003cp\u003eMedical students, as future clinicians and healthcare leaders, are key stakeholders in the clinical implementation of AI technologies. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) Assessing their readiness to embrace AI is crucial for successful adoption and its value in the medical field (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e–\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Evaluating medical students’ AI readiness involves assessing their understanding, attitudes, and perceived significance of AI in medicine (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). It is also important to address ethical considerations, health equity, and data security concerns related to AI (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e\u003ch3\u003eAttitudes and Perceptions\u003c/h3\u003e\u003cp\u003eMedical students generally hold positive attitudes toward AI in healthcare, with many recognizing its potential to improve medical outcomes (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e–\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). A survey of medical students in Kancheepuram District, Tamil Nadu, revealed that 85% of participants were aware of AI, and 98% believed it could improve healthcare (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Most students (71%) felt that AI teaching would benefit their careers, and 69.44% agreed that all students should receive AI training (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Despite these positive attitudes, some students express concerns about the impact of AI on employment prospects and ethical issues (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e–\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). For example, a study showed that 32.55% of students were less likely to consider a career in radiology due to the advancement of AI (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy design\u003c/h2\u003e\u003cp\u003eThis was a descriptive cross-sectional study with a survey-based approach, conducted during the second academic semester of 2024–2025 at Ahvaz Jundishapur University of Medical Sciences.\u003c/p\u003e\u003ch3\u003eSetting and participants\u003c/h3\u003e\u003cp\u003eThe study population included students enrolled in general medicine, dentistry, and pharmacy programs. Using a purposive sampling approach, 321 students were recruited. The inclusion criteria were willingness to participate in the study and prior exposure and familiarity with the study topic. Exclusion criteria were incomplete questionnaire responses and withdrawal of consent at any stage.\u003c/p\u003e\u003ch2\u003eData collection instruments\u003c/h2\u003e\u003cp\u003eThe data collection instrument was a standardized \"AI Readiness\" scale, consisting of 22 questions across four main dimensions: cognitive readiness, competency, perspective, and ethics. Questions were scored on a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree). The reliability of the scale was confirmed by Karaca (2021) with a Cronbach's alpha coefficient of 0.87 (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Its reliability and validity have also been established in local studies, with Reza Zadeh et al. (2023) reporting a Cronbach's alpha of 0.94 and content validity indices of 0.92 and 0.75 respectively (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eData collection procedure\u003c/h2\u003e\u003cp\u003eData were collected both in-person and online via Google Forms. Online questionnaires were distributed through student Telegram groups. All participants provided written informed consent and took part in the study voluntarily.\u003c/p\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eAfter verifying data adequacy and confirming internal consistency of the instrument using Cronbach’s alpha, the dataset was subjected to descriptive and inferential statistical analysis. Descriptively, demographic characteristics such as gender and prior familiarity with artificial intelligence were summarized using frequencies, percentages, means, and standard deviations. Central tendency and dispersion indices were also calculated for each readiness domain, and visualized through tables and graphs to enhance interpretability. Inferentially, both parametric (one-sample t-test, Pearson correlation) and non-parametric tests (Wilcoxon signed-rank test, Spearman correlation) were employed to test the study hypotheses and explore associations between variables. The choice of statistical procedures was guided by the results of the Kolmogorov–Smirnov test, which indicated that while some variables met the assumption of normality, others did not. Accordingly, appropriate methods were selected based on the distributional properties of the data. All statistical analyses were performed using SPSS (version 26), with the results presented in tabular format.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis section presents the statistical findings derived from the analysis of the collected data, structured around the main components of AI readiness. Both descriptive and inferential statistics are reported to provide a comprehensive understanding of students' preparedness for digital transformation in healthcare.\u003c/p\u003e\u003ch2\u003eParticipant Demographics\u003c/h2\u003e\u003cp\u003eA total of 321 students participated in the study, of whom 55.6% were male and 44.5% were female (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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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 the Participants\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\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.6%\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eCognitive Readiness\u003c/h2\u003e\u003cp\u003eDescriptive analysis for the Cognitive Readiness for AI Application component revealed a mean score of 3.033 (SD = 0.754), with a median of 3.125 and scores ranging from 1.00 to 5.00. The skewness value of − 0.009 indicated a nearly symmetric distribution. Although the Kolmogorov–Smirnov test indicated a statistically significant result (K–S = 0.030; p \u0026lt; 0.05), the distribution was sufficiently close to normal (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Given this near symmetry and the large sample size, parametric tests were considered appropriate for the inferential analysis .\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eDescriptive Statistics for the “Cognitive Readiness” Component\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSkewness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eK-S ‌\u003c/p\u003e\u003cp\u003eK-S Sig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eA one-sample t-test was conducted to compare the cognitive readiness scores against the test value of 3. The result (t(320) = 0.782, p = 0.435) indicated no statistically significant difference from the neutral benchmark (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This indicates that students' conceptual and theoretical understanding of artificial intelligence is generally balanced, reflecting a moderate level of confidence and no significant gaps in their cognitive readiness to engage with AI in medical contexts.\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\u003eOne-Sample t-Test Results for the “Cognitive Readiness” Component\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\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSig. (2-tailed)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean Diff\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI Lower\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI Upper\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.0499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1158\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eCompetency in AI Application\u003c/h2\u003e\u003cp\u003eDescriptive analysis for the 'Competency in AI Application' dimension revealed a mean score of 3.440 (SD = 0.818), with a median of 3.625 and a score range spanning from 1.00 to 5.00. The negative skewness value (–0.784) indicates a slight leftward skew in the distribution, suggesting that more participants rated themselves above the neutral midpoint. The Kolmogorov–Smirnov test for normality yielded a statistic of 0.000, indicating a significant deviation from normal distribution (p \u0026lt; 0.01).This violation of normality assumptions justifies the use of non-parametric tests, such as the Wilcoxon signed-rank test, for further inferential analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics for the “Competency in AI Application” Component\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSkewness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eK-S \u003c/p\u003e\u003cp\u003eK-S Sig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3.440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.784\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eTo evaluate students’ perceived competency in applying artificial intelligence, the Wilcoxon signed-rank test was employed to compare observed scores against the neutral benchmark value of 3. Out of 305 responses, 232 were greater than 3 and 70 were below. The test produced a Z value of 11.20 with a significance level of p = 0.000, confirming a statistically significant deviation from the midpoint (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This finding indicates that participants rated their AI competency significantly above average. The positive shift in scores reflects a general sense of confidence among students in using AI tools within academic or clinical settings, despite potential variation in actual skill levels.\u003c/p\u003e\u003cp\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=\"char\" char=\".\" 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=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWilcoxon Signed-Rank Test Results\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\u003eObservation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSum of Ranks\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWilcoxon Statistic (Z)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificance Level (p-value)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreater than 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40606.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10884.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eVision Toward AI Application\u003c/h2\u003e\u003cp\u003eDescriptive statistics for the 'Vision Toward AI Application' component revealed a mean score of 3.313 (SD = 0.923), with a median of 3.333 and scores ranging from 1.00 to 5.00. The skewness value of − 0.562 indicates a slight negative skew, suggesting that more students rated this dimension above the neutral midpoint. The Kolmogorov–Smirnov test yielded a significance level of p = 0.000, indicating that the data distribution significantly deviates from normality (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). As a result, non-parametric tests were employed for further analysis.\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics for the “Vision Toward in AI Application” Component\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSkewness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eK-S \u003c/p\u003e\u003cp\u003eK-S Sig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3/313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe Wilcoxon signed-rank test was conducted to compare the median vision scores against the test value of 3. Out of 266 participants, 184 had scores above 3 and 82 had scores below. The test produced a Z value of 14.19 with a p-value less than 0.001, indicating a statistically significant positive deviation (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). These findings suggest that students, on average, hold a significantly positive outlook toward the application of artificial intelligence in medicine. However, the wide range of scores (from 1 to 5) may reflect substantial variation in students’ experiences, expectations, or levels of awareness regarding AI in medical contexts.\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=\"char\" char=\".\" 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=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWilcoxon Signed-Rank Test Results\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\u003eObservation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSum of Ranks\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWilcoxon Statistic (Z)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreater than 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35,558.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14,582.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eEthical Awareness in AI Usage\u003c/h2\u003e\u003cp\u003eDescriptive statistics for the Ethical Readiness for AI Application component revealed a mean score of 3.690 (SD = 0.832), with a median of 4.00 and scores ranging from 1.00 to 5.00.The skewness value of − 0.789 reflects a moderate left skew, suggesting that most students rated themselves relatively high in terms of ethical awareness.The Kolmogorov–Smirnov test confirmed a significant deviation from normal distribution (p \u0026lt; 0.001), supporting the use of non-parametric tests for inferential analysis (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics for the “Ethical Awareness in AI Usage” Component\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSkewness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eK-S \u003c/p\u003e\u003cp\u003eK-S Sig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3/690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eA Wilcoxon signed-rank test was conducted to determine whether students' ethical readiness scores significantly exceeded the benchmark value of 3. Among the 266 participants, 184 reported scores above 3 and 82 below 3, resulting in a test statistic of Z = 12.04 (p \u0026lt; 0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). This indicates a statistically significant positive deviation, meaning that students, on average, possess a strong ethical orientation regarding the use of AI in medical practice. This relatively high level of ethical sensitivity may reflect an increasing awareness of digital ethics within the medical education environment. However, the notable standard deviation implies variability in individual levels of ethical understanding suggesting that while many students are ethically conscious, others may still possess inconsistent or superficial ethical insight.\u003c/p\u003e\u003cp\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=\"char\" char=\".\" 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=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWilcoxon Signed-Rank Test Results\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\u003eObservation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSum of Ranks\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWilcoxon Statistic (Z)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreater than 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32,886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11,751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eCorrelations\u003c/h2\u003e\u003cp\u003eThe following two tables present the bivariate correlations among the four principal constructs of the questionnaire. (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e) displays the Pearson correlation coefficients, whereas (Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e) reports the Spearman’s rank-order correlation coefficients as a non-parametric alternative.\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=\"char\" char=\".\" 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=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePearson correlation matrix among core questionnaire components; *p \u0026lt; 0.01\u003csup\u003e1\u003c/sup\u003e\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\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\u003eCompetency\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\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\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.729**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.667**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.440**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompetency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.729**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.707**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.606**\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\u003e0.667**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.707**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.563**\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\u003e0.440**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.606**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.563**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe results of Pearson correlation analysis indicated a statistically significant and positive relationship among all four components of AI readiness (p \u0026lt; 0.01). The strongest correlation was observed between Cognition and Competency (r = 0.729), suggesting that students with higher conceptual understanding of AI are more likely to feel competent in using it. Moreover, Competency showed a strong association with both V (r = 0.707) and Ethics (r = 0.606), highlighting the central role of practical skills in shaping positive perceptions and ethical awareness. The weakest correlation was found between Cognition and Ethics (r = 0.440), implying that conceptual knowledge alone may not strongly predict ethical sensitivity, and that experiential or value-based training may be required to bridge this gap.\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=\"char\" char=\".\" 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=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpearman correlation matrix among core questionnaire components\u003csup\u003e2\u003c/sup\u003e\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\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\u003eCompetency\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\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\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.660**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.586**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.349**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompetency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.660**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.629**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.512**\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\u003e0.586**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.629**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.482**\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\u003e0.349**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.512**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.482**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe Spearman correlation analysis similarly revealed statistically significant and positive relationships among all core components of the questionnaire (p \u0026lt; 0.01). The strongest association was found between Cognition and Competency (rₛ = 0.660), underscoring the foundational role of conceptual understanding in shaping functional capabilities. Competency also showed strong correlations with both Vision (rₛ = 0.629) and Ethics (rₛ = 0.512), suggesting that perceived ability plays a pivotal role in shaping values and perspectives. The weakest, albeit significant, correlation was observed between Cognition and Ethics (rₛ = 0.349), indicating a potential disconnect between theoretical knowledge and ethical considerations in AI. These findings support the internal coherence of the questionnaire dimensions while underscoring the importance of integrative education in connecting conceptual and ethical domains.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides important insights into the cognitive readiness, practical competence, Vision, and ethical awareness of medical students regarding the application of AI in healthcare. The findings reveal notable strengths as well as critical gaps, offering valuable insights for curriculum development and the design of future training programs. The findings of this study reveal that medical students possess only a moderate level of familiarity with fundamental AI concepts, and they demonstrate notable weaknesses in understanding more specialized processes and practical applications of AI in the healthcare domain. This limited and unstructured familiarity indicates an insufficient readiness to engage effectively in professional environments increasingly shaped by AI technologies. Contributing factors may include the lack of formal instruction, inadequate interdisciplinary content, and a persistent gap between foundational sciences and applied technologies in current medical education systems. These findings are consistent with prior research by Hamdani et al. (2023) and Lee \u0026amp; Chin (2023), which emphasized students\u0026rsquo; limited knowledge and cautious attitudes toward AI integration (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). In contrast, studies by Giovanner (2022) and Bisdas (2021) report more favorable perceptions and broader acceptance of AI, likely influenced by differences in educational approaches, resource availability, and cultural contexts (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).The significance of these findings lies in highlighting a systemic shortfall: current medical education frameworks have yet to integrate AI as a central, future-oriented component. This inadequacy not only hinders the effective implementation of emerging technologies in healthcare delivery, but also risks widening the gap between academic instruction and clinical practice. Therefore, the development and integration of interdisciplinary curricula, hands-on workshops, and dedicated academic modules on health-related technologies must be recognized as a strategic educational priority. Regarding students' AI-related competencies, the findings suggest that their proficiency in utilizing AI tools particularly in medical education, healthcare services, and research is above average. This relative strength is most evident in routine engagement with digital health applications and purposeful use of online resources. However, their analytical skills and clinical decision-making abilities in applying AI remain underdeveloped. These findings suggest that students\u0026rsquo; competencies are largely practice-based and are not significantly influenced by demographic factors such as age or years of study. This is in line with studies by Lee \u0026amp; Chin (2023)and Sabet et al. (2023), which found that hands-on experience with AI strongly correlates with enhanced user competence and conceptual understanding (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Similar trends were observed in studies by Ali \u0026amp; Moghari (2024) and Laupichler et al. (2024), indicating that while students demonstrate an acceptable level of AI literacy in practical domains, their understanding of algorithmic errors and technical intricacies remains limited (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). This alignment suggests that existing training programs tend to emphasize applied and general uses of AI, without adequately addressing analytical and decision-making competencies. Conversely, the study by Beigi et al. (2022) reported lower practical skills, possibly reflecting the limited exposure to research opportunities among their participants (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). The discrepancy with Sabavierpandian et al. (2024) in Zambia may be attributed to infrastructural differences and restricted access to AI education in low-resource settings (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Students\u0026rsquo; attitudes toward the application of AI in medicine were generally positive and optimistic. This favorable outlook is particularly evident in their ability to anticipate the potential benefits and risks of AI, although less so in their understanding of its technical limitations. These results echo those of Beigi et al. (2022), where most health students regarded AI as a beneficial tool for the future of medicine (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). However, this optimism appears to be shaped more by general exposure to digital media than by practical or technical experience with AI systems. This trend aligns with Santos (2019), who also found strong support among students for AI integration in education and healthcare delivery (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The data further suggest that even minimal exposure to AI-related technologies can contribute to a more favorable attitude toward their use. This observation aligns with the findings of Sabet et al. (2023) and Mirzaei \u0026amp; Askouei (2022) and, which showed that participation in structured training programs or hands-on experiences significantly enhances not only students\u0026rsquo; knowledge but also their attitudes toward AI (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).In terms of ethical considerations, students demonstrated relatively high awareness of AI ethics, particularly concerning the legal and moral use of health data. This finding is consistent with prior studies by Sabet et al. (2023) and Beigi et al. (2022) and, which reported improvements in students' ethical understanding following targeted education (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Nevertheless, although overall ethical awareness was adequate, students scored lower on items related to regulatory compliance and national legal standards. This suggests a gap between general ethical values such as data confidentiality and fairness and concrete knowledge of local legal frameworks. Addressing this gap requires integrating legal literacy and policy training into medical curricula to ensure that future professionals can effectively apply ethical principles within real-world digital health ecosystems.\u003c/p\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Recommendations\u003c/h2\u003e\u003cp\u003eThis study had certain limitations that should be acknowledged. The cross-sectional design limits causal inference, and the findings are based on self-reported data, which may be subject to social desirability or recall biases. Moreover, the generalizability of the results may be limited due to the specific demographic and institutional contexts of the participants. In light of the findings, there is a pressing need to develop structured interdisciplinary educational programs, practical workshops, and dedicated academic modules focused on health technologies and artificial intelligence within the medical education system. Emphasizing hands-on experience and targeted training can help bridge the existing gap between theoretical knowledge and practical competence, ultimately enabling more effective integration of emerging technologies in healthcare delivery.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings of this study, encompassing four pivotal constructs cognitive readiness, competency, outlook, and ethics in the application of artificial intelligence indicate that although students exhibit foundational preparedness in certain areas, their overall readiness to effectively implement AI technologies within clinical, educational, and research domains remains suboptimal. Notable strengths include practical skills and a generally favorable vision towards AI applications. Nevertheless, significant gaps persist in the comprehensive understanding of specialized AI concepts, analytical capabilities, and awareness of associated legal and regulatory frameworks, underscoring the urgent eed for well-structured and focused educational programs. While students display familiarity with basic concepts such as statistics and data management, this level of cognitive understanding falls short of enabling them to grasp the intricate workings of AI algorithms and predictive models. Competency-wise, although some proficiency in utilizing relevant software and integrating AI into clinical practice exists, limitations remain in selecting appropriate tools to address complex healthcare challenges. The prevailing outlook is positive and future-oriented; however, it is largely influenced by general perceptions rather than grounded practical knowledge. Ethically, a foundational commitment is evident, but the ability to discern and manage nuanced ethical issues arising from emerging AI technologies is insufficient. Consequently, it is imperative that educational and research institutions implement comprehensive, multidimensional training initiatives aimed at advancing specialized knowledge, analytical skills, and ethical-legal literacy concerning AI. Such measures are essential to safely and effectively harness the full potential of AI innovations across the healthcare, education, and research sectors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eArtificial Intelligent: AI\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003e This study was designed and conducted in accordance with the ethical principles of the Declaration of Helsinki. Ethical approval was obtained from the Research Ethics Committee of Ahvaz Jundishapur University of Medical Sciences. (Ethics Code: IR.AJUMS.REC.1403.432) prior to the commencement of the study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eB.GH.H. and E.A.M. conceived and designed the study. E.A.M., B.GH.H., and J.Z. contributed to the development of the methodology. J.Z., A.B., and B.GH.H. performed the data analysis, while A.B. was responsible for data collection. B.GH.H. drafted the original manuscript. All authors contributed to the interpretation of the results, critically revised the manuscript for important intellectual content, and approved the final version for publication. Supervision was provided by E.A.M.- Conceptualization: E.A.M., B.GH.H.- Methodology: E.A.M., B.GH.H., J.Z.- Formal analysis: J.Z., A.B., B.GH.H.- Investigation (data collection): A.B.- Writing \u0026ndash; original draft: B.GH.H.- Writing \u0026ndash; review \u0026amp; editing: All authors- Data interpretation: All authors- Supervision: E.A.M.- Final approval of the manuscript: All authors\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\u003cp\u003eThe datasets generated and /or analyzed during the current study are available from the corresponding author on reasonable request. Due to privacy and ethical restrictions related to participant confidentiality, the data are not publicly available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, Aldairem A, Alrashed M, Bin Saleh K, Badreldin HA, Al Yami MS. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC medical education. 2023 Sep 22;23(1):689. https://doi.org/10.1186/s12909-023-04698-z\u003c/li\u003e\n\u003cli\u003ePinto dos Santos D, Giese D, Brodehl S, Chon SH, Staab W, Kleinert R, Maintz D, Bae\u0026szlig;ler B. Medical students\u0026apos; attitude towards artificial intelligence: a multicentre survey. European radiology. 2019 Apr 1;29:1640-6. https://doi.org/10.1007/s00330-018-5601-1\u003c/li\u003e\n\u003cli\u003eMaa\u0026szlig; L, Grab-Kroll C, Koerner J, \u0026Ouml;chsner W, Sch\u0026ouml;n M, Messerer DA, B\u0026ouml;ckers TM, B\u0026ouml;ckers A. 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Journal of English Language Teaching and Learning. 2022;14(30):165-79. https://doi.org/10.22034/elt.2022.52038.2496\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e \u003cb\u003eAll correlation coefficients were significant at the 0.01 level, with p-values equal to 0.000\u003c/b\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cb\u003eAll coefficients are statistically significant at the 0.01 level (two-tailed)\u003c/b\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Cognitive Readiness, Technology Acceptance, Student Attitudes, Medical Education","lastPublishedDoi":"10.21203/rs.3.rs-7313375/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7313375/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eArtificial intelligence (AI) is rapidly transforming healthcare by enhancing diagnostic accuracy, enabling personalized treatments, and improving patient outcomes. Medical students, as future healthcare providers and primary AI users, require adequate knowledge and readiness to integrate AI effectively in clinical practice. Despite growing global interest, little is known about the preparedness of medical students in Iran to adopt AI technologies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA descriptive cross-sectional survey was conducted among 321 students from medicine, dentistry, and pharmacy programs at Ahvaz Jundishapur University of Medical Sciences during the 2024\u0026ndash;2025 academic year. Data were collected via a validated 22-item AI readiness scale covering four domains: cognition, competency, vision, and ethics. Descriptive and inferential statistics, including one-sample t-tests and Wilcoxon signed-rank tests, were applied based on data distribution. Correlation analyses explored relationships among readiness components.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResult\u003c/b\u003e\u003c/p\u003e\u003cp\u003eParticipants demonstrated moderate cognitive readiness (mean\u0026thinsp;=\u0026thinsp;3.03), indicating an average theoretical understanding of AI. Competency in AI application scored significantly above average (mean\u0026thinsp;=\u0026thinsp;3.44, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reflecting confidence in practical use, particularly with digital health tools. The vision toward AI in medicine was positive (mean\u0026thinsp;=\u0026thinsp;3.31, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), although varied among students. Ethical awareness scored highest (mean\u0026thinsp;=\u0026thinsp;3.69, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating strong sensitivity to AI\u0026rsquo;s ethical challenges. Significant positive correlations were found among all domains (r\u0026thinsp;=\u0026thinsp;0.44 to 0.73, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with the strongest between cognition and competency. Despite general optimism, gaps remain in technical knowledge and regulatory understanding.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMedical students in this cohort demonstrate encouraging readiness to engage with AI, particularly in practical and ethical domains; however, foundational knowledge and technical literacy need to be strengthened. The findings underscore the urgent need to integrate interdisciplinary AI education, hands-on training, and legal-ethical instruction into medical curricula. These initiatives are essential to prepare future healthcare professionals for effective and responsible AI integration, ultimately enhancing the quality of patient care.\u003c/p\u003e","manuscriptTitle":"Assessing the level of readiness for digital transformation in medicine: Students of Ahvaz Jundishapur University of Medical Sciences for the use of artificial intelligence in health","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-02 14:24:00","doi":"10.21203/rs.3.rs-7313375/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-08T07:54:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-02T17:51:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-28T21:10:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-27T04:56:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133259980818826043885441825859974935190","date":"2025-08-26T19:25:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211727401445927146765240270343567176558","date":"2025-08-26T08:33:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7433490278183460864927243670345480454","date":"2025-08-26T08:22:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-26T08:13:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-18T05:34:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-15T03:48:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-08-13T10:38:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0989c840-5fa8-48a2-ba30-00877a636731","owner":[],"postedDate":"September 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:03:15+00:00","versionOfRecord":{"articleIdentity":"rs-7313375","link":"https://doi.org/10.1186/s12909-026-08865-w","journal":{"identity":"bmc-medical-education","isVorOnly":false,"title":"BMC Medical Education"},"publishedOn":"2026-02-23 15:59:31","publishedOnDateReadable":"February 23rd, 2026"},"versionCreatedAt":"2025-09-02 14:24:00","video":"","vorDoi":"10.1186/s12909-026-08865-w","vorDoiUrl":"https://doi.org/10.1186/s12909-026-08865-w","workflowStages":[]},"version":"v1","identity":"rs-7313375","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7313375","identity":"rs-7313375","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}