Readiness and Perceptions toward Artificial Intelligence among medical students in Egypt

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Limited research exists on their awareness, skills, and perception toward AI in medicine. This study evaluates AI readiness and perceptions among medical students at Port Said University. Objective To assess Readiness towards Artificial Intelligence among medical students in Egypt. To assess Perception towards Artificial Intelligence among medical students in Egypt. Methods A cross-sectional study was conducted among medical students from 8 Egyptian Universities, selected through convenient sampling. The Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) was used to assess students’ readiness. The Perception of Artificial Intelligence in Medical Students (PAIMS) scale was used to evaluate students’ perceptions of AI. Data were collected using online self-administered questionnaires to assess AI readiness and perceptions. Data analysis was performed using SPSS version 25 . Results A total of 356 responses were collected. The median total readiness score (MAIRS-MS) was 66.0 (IQR: 26.0). Among the readiness domains, Ability had the highest median score (24.0/40). Cognition scores varied significantly across years of study (p = 0.012) and among students who had attended an AI course (16%, p = 0.008). No significant differences in AI readiness scores were observed across universities. The median overall PAIMS score was 2.25 (IQR: 0.67), with the Knowledge and Trust domain having a median of 2.6 (IQR: 1). No significant differences in AI perceptions were observed across student characteristics. Conclusion Students from Egyptian universities demonstrated moderate readiness for AI integration, with strengths in practical ability and variable cognition influenced by academic year and prior AI training. Their perceptions of AI were generally positive and consistent across student groups. These findings can inform curriculum development by identifying areas where targeted AI education and training are needed, supporting the incorporation of AI tools into medical education, and preparing future physicians to effectively engage with AI in clinical practice. Artificial Intelligence Readiness Perception Medical student Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Artificial intelligence (AI) is an interdisciplinary field that specializes in the creation of intelligent machines. These machines are capable of problem solving and learning through many methods, including machine learning, neural networks, and natural language processing. It is promising that AI will refine human decisions and improve overall effectiveness in countless areas. [ 1 ] Although the history of AI can be traced back to the 1950s, the public’s unrestricted access to highly advanced large language models can be seen as a significant turning point in the history of AI. Early studies demonstrated that ChatGPT is capable of successfully completing the written portion of the U.S. Medical Licensing Examination. Given the capabilities of AI-based chat applications in medicine, further studies have highlighted their potential use in providing information on cancer, assisting in clinical diagnoses, authoring scientific research articles, and assisting patient communication. Considering the wide availability and integration of medical knowledge in this application, its increasing use in medicine and among medical students is foreseeable. [ 2 ] As in the field of medicine, AI, often referred to as artificial intelligence in medicine, has revealed its potential; it can classify medical images such as X-rays and offer capabilities for prognosis, treatment planning, patient monitoring, etc. A number of AI applications have been found to improve different aspects of clinical practice, such as improving medication compliance and reducing outpatient clinic waiting times. [ 3 ] Considering the likely significant impact of the implementation and use of AI in medicine, a growing body of literature advocates the inclusion of AI-related content in medical curricula. In addition to implications for the medical profession and patient care, medical students are expected to face new ethical challenges posed by the use of AI in medicine. [ 4 ] Despite the potentially significant ethical challenges anticipated from the deployment of AI in medicine, such as the possibility of discrimination due to biases in the data used for training or effects on patient autonomy, there is a near-complete absence of scientific publications on specific teaching content, readiness or perceptions of AI in medical education.[ 5 ] AI is revolutionizing healthcare through applications such as machine learning for diagnostic imaging, natural language processing for EHRs, and predictive analytics for patient outcomes. Understanding these applications is essential for medical students to appreciate the potential and limitations of AI. [ 6 ] Health information technologies (E-health programs) are increasingly used to support the communication demands of health care delivery and health promotion, helping both to provide consumers with relevant health information and to gather information from consumers. However, the quality of communication with these technologies is crucial for providing consumers and providers with accurate, timely, sensitive, and adaptive health information. If there is a breakdown in the ways health risks, treatment regimens, and recommended health behaviors are communicated, there will be problems with encouraging consumers to accept and incorporate health recommendations (such as following therapeutic procedures, taking prescribed medications, and adopting health promotion strategies). [ 3 ] AI helps physicians better identify patients who need extra attention and provides personalized protocols for each individual on a computer application. AI can be used by primary care physicians to analyze their discussions with patients, create notes and have the necessary information entered directly into the EHR system. Several advantages of using AI in medicine include efficiency, accuracy, precision, decreased workload, increased patient face time, increased time in critical cases, reduced money, and better monitoring. [ 7 ] Physicians have responsibilities that go beyond their clinical duties, encompassing leadership and health advocacy. The transformative potential of AI in healthcare presents important ethical and operational challenges that physicians need to address collaboratively to safeguard patient welfare. [ 6 ] AI can be utilized in clinical diagnosis, especially in regions facing a shortage of medical doctors. AI can also be used to decrease human error in image processing in radiology and histopathology. Additionally, it can be used to interpret electrocardiograms (ECGs) in cases of atrial fibrillation, ventricular tachyarrhythmia, and myocardial infarction. AI can also be applied to detect sleep disorders and epilepsy, analyze electromyography (EMG) data, and conduct Doppler ultrasound assessments for patients in intensive care units (ICUs). [ 8 ] Medical students are crucial for the future of healthcare, yet there are limited opportunities for them to learn about AI. To successfully integrate AI into healthcare, medical education must be reformed to include comprehensive training on its benefits and ethical implications. [ 8 ] Organizations such as the World Medical Association support updating curricula, and national surveys on students’ attitudes toward AI can help identify knowledge gaps and inform effective training programs. [ 9 ] Preparing for AI in medicine involves more than acquiring information technology skills such as programming. A solid understanding of basic and clinical medicine is crucial for effectively leveraging AI, along with expertise in data science, biostatistics, and evidence-based medicine. While AI advancements primarily benefit high-income countries, there is limited discussion on the impact of AI in low- and middle-income countries, where healthcare access is hindered by resource shortages. [ 10 ] We conducted this study to assess the readiness and perceptions of medical students in Egypt regarding artificial intelligence in healthcare. As AI has reshaped medical practices, it is essential for future healthcare professionals to understand its applications. However, there is a lack of research on students' preparedness to embrace AI, which limits our understanding of their awareness and skills. This study aims to eliminate any misunderstandings and uncertainties about artificial intelligence among medical students by helping them fully understand the role of AI technology in healthcare. Methods Study Design, Setting, and Population : An analytical, cross-sectional study design was utilized to assess the readiness and perceptions of medical students toward AI comprehensively. To ensure a representative evaluation of the Egyptian medical education landscape, the study was conducted as a multicenter survey involving students from eight distinct medical faculties: Port Said University, Damietta University, Mansoura University, Horus University, Ain Shams University, Helwan University, Al-Azhar Damietta University and Kafr El-Sheikh University. The study population comprised undergraduate medical students currently enrolled in these institutions, selected via a convenient nonrandom sampling technique to facilitate efficient data collection across diverse geographical settings. Sample size : The minimum required sample size was rigorously determined to ensure statistical power on the basis of the following formula [ 11 ]: \(\:n=(Z\times\:\sigma\:/E{)}^{2}\) . The calculation utilized a standard normal deviation ( \(\:Z\) ) of 1.96 for a 95% confidence interval. The population standard deviation ( \(\:\sigma\:\) ) for the mean score of readiness was estimated at 14.8, a figure derived from a comparable reference study conducted among medical students in Malaysia. With the margin of error ( \(\:E\) )[ 3 ] set at 1.67, the calculated minimum sample size was established at 301 medical students. This threshold ensures that the study results will be statistically generalizable to the target population. Data collection : Data were collected via a structured, online self-administered questionnaire designed to evaluate the students' stance on AI through three specific dimensions: 1. Sociodemographic profile : The initial section captures baseline characteristics, including age, gender, university affiliation, year of study, and whether the participant has received any prior academic coursework regarding AI in medicine. 2. Readiness Assessment (MAIRS-MS) [ 12 ]: The second section employs the validated Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). This 22-item instrument measures readiness across four distinct domains: Cognition (8 items), Ability (8 items), Vision (3 items), and Ethics (3 items). Responses are recorded on a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), with higher aggregate scores indicating a higher level of preparedness for AI integration. 3. Perception assessment [ 13 ]: The third section assesses student perceptions via a 12-item scale adapted from previous research conducted in Kerala. This scale is categorized into three domains: knowledge and trust, informed self-control, and disadvantages and risks. The responses utilized a 5-point Likert scale (0 to 4). To ensure analytical accuracy, the five statements related to "Disadvantages and Risks" are reverse-coded; consequently, higher scores across all the domains consistently reflect a more positive overall perception of AI. Data Management: Clean and filter the collected data. Each variable was coded to streamline data transfer: In this study, two scales were used to assess medical students’ readiness and perceptions toward AI. The first tool is MAIRS-MS, which consists of 22 statements distributed across four domains: cognition, ability, vision, and ethics. Each item is coded on a 5-point Likert scale ranging from 1 (strongly disagree) to 5. (strongly agree), with higher scores reflecting greater readiness for AI integration in medical practice. The second tool assessed students' perceptions of AI through 12 statements grouped under three domains: knowledge and trust, informed self-control, and disadvantage and risk. These items are scored on a 5-point Likert scale ranging from 0 (totally disagree) to 4 (totally agree). Importantly, 5 statements related to the disadvantages and risks of AI are reverse-coded as follows ensure that all items measured perception in the same direction, with higher scores indicating a more positive overall perception of AI. • These codes were input into the Statistical Package for Social Sciences (SPSS) version 25, where all the statistical analyses were conducted. • The data analysis process consists of: o Descriptive statistics were utilized, presenting quantitative data as medians (IQRs) and qualitative data as numbers (with percentages). o Graphs and tables will be employed where appropriate. The Kolomogrov-Smirnov test was conducted to assess data normality. o Associations between outcome measures and various program components were tested for significance via the Kruskal‒H test for continuous data with a normal distribution, whereas the Mann‒Whitney rank sum test was used for nonparametric data. o Statistical significance was assessed at a 95% confidence level (i.e., differences will be deemed significant if P < 0.05). o Data presentation was tailored according to the type of variables. Ethical considerations Strict ethical standards were maintained throughout the research process. Official ethical approval was secured from the Faculty of Medicine at Port Said University. The study's design ensures the protection of participants' rights; an informed consent section is embedded at the beginning of the online form, clearly outlining the research objectives, the voluntary nature of participation, and the right to withdraw at any time without justification. To guarantee privacy, all the data were treated with strict confidentiality and anonymized during the cleaning process, and no personal identifiers were published. Results A total of 356 medical students from Egypt were included in this part of the study, as indicated in Table 1 . The participants were predominantly male (69.7%), with a median age of 21 years. The student sample was drawn from eight universities, with nearly half (48.6%) hailing from Port Said University. The participants were distributed across their second to fifth years of study, with the largest group being fifth-year students (32.9%). A key demographic finding is that a vast majority of the students (84.0%) had no prior formal training in AI, indicating limited exposure to the subject within their curriculum. Table 1 Sociodemographic characteristics of the participants (n = 356) Variable n (%) Age Median (IQR) 21 (2) Gender Male 248 (69.7%) Female 108 (30.3%) University Port Said University 173 (48.6%) Damietta University 80 (22.5%) Horus University 46 (12.9%) Mansoura University 33 (9.3%) Kafr Elsheikh University 13 (3.7%) Ain Shams University 5 (1.4%) Helwan University 3 (0.8%) Al-Azhar Damietta University 3 (0.8%) Year of Study Second Year 68 (19.1%) Third Year 81 (22.8%) Fourth Year 90 (25.3%) Fifth Year 117 (32.9%) Attended AI Course No 299 (84.0%) Yes 57 (16.0%) Regarding the MAIRS-MS scale, as presented in Table 2 , the findings reflect the level of AI readiness among medical students. The overall median score on the scale was 66.00, indicating a moderate level of general readiness for AI in medicine. An analysis of the specific domains revealed a distinct pattern. The participants scored highest in the ability (median 24.00) and cognition (median 23.00) domains, as detailed in the itemized responses for Fig. 1 (Cognition) and Fig. 2 (Ability). The scores for the 'Vision' and 'Ethics' domains were also moderate, with a median of 9.00; the distribution of student responses for these domains is presented in Fig. 3 and Fig. 4 , respectively. Table 2: AI readiness scale scores (n = 356) Domain Median (IQR) Cognition(8–40) 23.00 (10.00) Ability(8–40) 24.00 (10.75) Vision(3–15) 9.00 (6.00) Ethics(3–15) 9.00 (6.00) Total MAIRS-MS 66.00 (26.00) Figure 4: Ethics domain Response on Medical Student’s AI Readiness (%) Table 3 provides an analysis of the factors associated with AI readiness. A statistically significant association was found between the year of study and the 'Cognition' domain score (p = 0.012), with second-year students reporting the highest median score. Most notably, having attended a prior AI course was also significantly associated with a higher score in the 'Cognition' domain (p = 0.008). This finding indicates that formal AI training directly enhances students' knowledge. Table 3 Association between demographic characteristics, AI training and AI readiness scale scores. Variable Category Cognition Ability Vision Ethics MAIRS-MS Gender Female 23.0 (10.75) 24.5 (10.75) 9.0 (6.0) 9.0 (6.0) 67.0 (31.0) Male 24.0 (10.0) 24.0 (11.0) 9.0 (5.0) 9.0 (5.0) 66.0 (26.0) P value* 0.988 0.691 0.479 0.189 0.548 University Ain Shams University 24.0 (15.5) 24.0 (12.0) 9.0 (5.5) 9.0 (5.5) 66.0 (23.0) Al-Azhar Damietta University 32.0 (–) 31.0 (–) 12.0 (–) 12.0 (–) 87.0 (–) Damietta University 21.0 (10.75) 24.0 (11.75) 9.0 (5.0) 9.0 (5.0) 63.0 (28.0) Helwan University 26.0 (–) 23.0 (–) 9.0 (–) 9.0 (–) 68.0 (–) Horus University 24.0 (13.0) 24.5 (11.0) 9.0 (5.25) 9.0 (5.25) 66.0 (32.25) Kafr Elsheikh University 26.0 (8.5) 29.0 (6.5) 11.0 (3.0) 11.0 (3.0) 76.0 (17.5) Mansoura University 24.0 (13.0) 28.0 (14.0) 10.0 (3.5) 10.0 (3.5) 68.0 (30.5) Port Said University 23.0 (10.0) 25.0 (10.0) 9.0 (5.5) 9.0 (5.5) 67 (24) P value+ 0.205 0.143 0.087 0.247 0.149 University level Second year 25.5 (8.75) 26.0 (7.75) 10.0 (4.0) 10.0 (4.0) 74.0 (19.75) Third year 23.0 (12.0) 24.0 (11.5) 9.0 (6.0) 9.0 (6.0) 66.0 (31.5) Fourth year 23.0 (11.0) 25.0 (12.25) 9.0 (6.0) 9.0 (6.0) 66.0 (26.5) Fifth year 22.0 (10.5) 24.0 (11.0) 9.0 (5.0) 9.0 (5.0) 66.0 (24.0) P value+ 0.012 0.615 0.256 0.501 0.130 Attended AI course No 23.0 (9.0) 24.0 (10.0) 9.0 (6.0) 9.0 (6.0) 66.0 (25.0) Yes 26.0 (14.5) 26.0 (13.5) 9.0 (6.0) 9.0 (6.0) 67.0 (37.0) P value* 0.008 0.308 0.766 0.874 0.168 P*: Statistical tests performed: Mann‒Whitney U test P+: Statistical test performed: Kruskal–Wallis H test Table 4 and Fig. 5 show their specific perceptions of AI in medicine. The findings reveal a dual perspective: students recognize the practical benefits of AI but also harbor significant concerns about its broader implications. There was strong agreement that AI facilitates physicians' access to information (70.2% agree/totally agree) and enables more accurate decisions (60.1% agree/totally agree). However, the students expressed notable reservations regarding the ethical and humanistic aspects of medicine. A significant portion of participants agreed that AI could reduce the humanistic aspect of the medical profession (44.1%) and that violations of professional confidentiality may occur more frequently (40.7%). This apprehension aligns with the low 'Ethics' and 'Vision' scores shown in Table 2, confirming that while students are open to AI as a technical tool, they remain cautious about its potential to disrupt the core values of medical practice. Table 4 Participants’ Perceptions of AI in Medicine. Statement Totally Agree Agree Unsure Disagree Totally Disagree Reduces errors in medical practice 48 (13.5%) 153 (43.0%) 87 (24.4%) 46 (12.9%) 22 (6.2%) Facilitates patients’ access to the service 65 (18.3%) 166 (46.6%) 72 (20.2%) 31 (8.7%) 22 (6.2%) Facilitates physicians’ access to information 90 (25.3%) 160 (44.9%) 60 (16.9%) 21 (5.9%) 25 (7.0%) Enables the physician to make more accurate decisions 66 (18.5%) 148 (41.6%) 76 (21.3%) 44 (12.4%) 22 (6.2%) Increases patients’ confidence in medicine 36 (10.1%) 106 (29.8%) 113 (31.7%) 73 (20.5%) 28 (7.9%) Knowledge and Trust Median (IQR) 2.6(1) Negatively affects the relationship of the physician with the patient 25 (7.0%) 76 (21.3%) 100 (28.1%) 104 (29.2%) 51 (14.3%) Devalues the medical profession 16 (4.5%) 77 (21.6%) 95 (26.7%) 111 (31.2%) 57 (16.0%) Damages the trust which is the basis of the patient–physician relationship 28 (7.9%) 90 (25.3%) 96 (27.0%) 98 (27.5%) 44 (12.4%) Reduces the humanistic aspect of the medical profession 41 (11.5%) 116 (32.6%) 87 (24.4%) 72 (20.2%) 40 (11.2%) Violations of professional confidentiality may occur more 31 (8.7%) 114 (32.0%) 113 (31.7%) 60 (16.9%) 38 (10.7%) Disadvantages and Risks Median (IQR) 2 (1.4) Allows the patient to increase their control over own health 43 (12.1%) 142 (39.9%) 107 (30.1%) 42 (11.8%) 22 (6.2%) Facilitates patient education 68 (19.1%) 160 (44.9%) 77 (21.6%) 26 (7.3%) 25 (7.0%) Informed Self-Control Median (IQR) 3(1) Total PAIMS Median (IQR) 2.25(0.67) Table 5 compares the median scores (with IQRs) for the PAIMS domains Knowledge & Trust, Disadvantages & Risks, and Informed Self-Control. Statistical tests (Mann‒Whitney U test for gender/AI courses; Kruskal‒Wallis H test for university/year) were used to assess differences, with p values indicating significance. Females (n = 108) had slightly lower knowledge and trust (2.6 vs. 2.8 for males, n = 248) and informed self-control (3.0 vs. 2.5) but similar disadvantages and risks (2.0), with no significant differences (p = 0.921, 0.114, 0.202, 0.474 overall), suggesting minimal gender variation. University scores varied (e.g., Ain Shams: 2.17 overall; Helwan: 3.0; Al-Azhar Damietta: 2.67), and second-year students presented the highest knowledge and trust scores (2.8) and informed self-control scores (3.0), with no significant year differences (p = 0.625, 0.790, 0.712, 0.493), implying early stabilization. AI course attendees (n = 56) had marginally lower Knowledge & Trust (2.6 vs. 2.6), Disadvantages & Risks (2.2 vs. 2.0), and Overall (2.17 vs. 2.33) but higher Informed Self-Control (2.5 vs. 3.0), with no significance (p = 0.353, 0.547, 0.212, 0.138), Overall medians (2.25–2.67), and IQR variability (0.58–0.75) Table 5 Comparison of median (IQR) scores for the PAIMS domains across participant characteristics. Characteristic Category Knowledge & Trust Disadvantages & Risks Informed Self-Control Overall (PAIMS) Gender Female 2.6 (1.0) 2.0 (1.15) 3.0 (1.0) 2.25 (0.75) Male 2.8 (1.0) 2.0 (1.20) 2.5 (1.0) 2.25 (0.67) p value* 0.921 0.114 0.202 0.474 University Ain Shams University 2.4 (1.1) 2.0 (0.6) 2.5 (1.0) 2.17 (0.58) Al-Azhar Damietta Univ. 3.0 (0.0) 2.2 (0.0) 3.0 (0.0) 2.67 (0.0) Damietta University 2.6 (1.15) 2.2 (1.40) 2.5 (1.0) 2.25 (0.73) Helwan University 2.4 (—) 3.0 (—) 3.5 (—) 3.0 (—) Horus University 2.4 (1.20) 2.0 (1.30) 2.5 (1.13) 2.08 (0.60) Kafr Elsheikh University 2.8 (0.70) 2.2 (0.60) 3.0 (1.0) 2.67 (0.50) Mansoura University 2.8 (0.80) 1.8 (1.40) 3.0 (1.0) 2.25 (0.67) Port Said University 2.8 (0.80) 2.0 (1.20) 3.0 (1.0) 2.25 (0.67) p value+ 0.255 0.093 0.730 0.160 Year of Study Second Year 2.8 (0.75) 2.0 (1.40) 3.0 (1.00) 2.33 (0.65) Third Year 2.6 (1.00) 2.0 (1.00) 2.5 (1.00) 2.25 (0.67) Fourth Year 2.6 (1.00) 2.0 (1.40) 2.5 (1.00) 2.29 (0.67) Fifth Year 2.8 (1.00) 2.0 (1.20) 3.0 (1.00) 2.25 (0.67) p value+ 0.625 0.790 0.712 0.493 Attended AI Course No 2.6 (1.00) 2.0 (1.20) 3.0 (1.00) 2.33 (0.67) Yes 2.6 (1.30) 2.2 (1.70) 2.5 (1.50) 2.17 (0.75) p value* 0.353 0.547 0.212 0.138 P*: Statistical tests performed: Mann‒Whitney U test P+: Statistical test performed: Kruskal–Wallis H test Discussion This study evaluated AI readiness among 356 Egyptian medical students via the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), yielding a median total score of 66.00 (IQR 26.00), which is indicative of moderate overall preparedness for integrating AI into medical practice. The study revealed moderate PAIMS perceptions (median 2.25–2.67 overall), with balanced knowledge/trust (2.4–3.0) and disadvantages/risks (2.0–2.2), but higher informed self-control (2.5–3.0), indicating cautious optimism. Gender differences were negligible, with females being more cautious about self-control (3.0 vs. 2.5). University comparisons revealed variability (e.g., Helwan 3.0 vs. Al-Azhar 2.67) but were not significant, suggesting shared curricula. Readiness Internationally, a 2022 US-based study by Long et al. surveyed 1,500 medical students across 20 institutions, reporting a mean AI readiness score of 65.4 (SD 12.3), closely aligning with our median of 66.00; they noted high 'Ability' scores (mean 24.8) but low 'Ethics' scores (mean 9.2), similar to our findings, although US students presented stronger 'Vision' scores (mean 12.5) due to curriculum integration at schools such as Harvard, where AI simulations are standard. This difference likely stems from infrastructural disparities, as developed nations invest in AI labs and simulations, enabling students to envision applications such as predictive analytics, whereas in Egypt, limited resources and sporadic training (84% untrained) hinder long-term perspectives, compounded by economic factors such as unequal access to technology. [ 14 ] Another U.S. study by Topol et al. in 2023 involving 800 students reported 62% agreement on the role of AI in accurate diagnostics, which is comparable to our 60.1% agreement, but greater concerns about job displacement (52% vs. our implicit 26.1% agreement with devaluation), attributed to advanced AI adoption in U.S. hospitals; this variation may reflect global educational gaps, where AI is taught sporadically, fostering technical skills but neglecting ethical training, leading to shared fears about humanism and confidentiality, as students perceive AI as a potential disruptor rather than an enhancer of core medical value. [ 15 ] Regionally, Sallam et al. 's 2023 Jordanian study of 1,200 medical students mirrored our results with a median readiness of 68.5 (IQR 25.0), high 'Ability' (median 25.0), and low 'Ethics' (median 9.5); perceptions showed 62% agreement on diagnostic accuracy and 45% worry about job loss, echoing our 56.5% and 26.1%, respectively, although Jordanians had slightly elevated 'Vision' scores, possibly from regional workshops. These similarities in moderate readiness and ethical apprehensions likely result from universal educational gaps, whereas regional variations, including greater ethical concerns in Middle Eastern studies (e.g., our 40.7% confidence), stem from cultural contexts; Islamic bioethics in Egypt and Jordan emphasize relational trust and privacy, amplifying worries about AI's impersonal nature, unlike Europe's regulatory familiarity. [ 16 ] In the Middle East, a 2024 Saudi Arabian study by Alghamdi et al. among 500 students reported a median readiness of 64.0, with 58% agreeing that AI enhances patient access (similar to our 64.9%), but higher ethical concerns (48% on confidentiality risks vs. our 40.7%), reflecting shared cultural emphasis on trust in physician‒patient relationships; this alignment underscores how cultural factors in the region amplify worries, potentially owing to shared religious and social norms prioritizing patient‒physician bonds. [ 17 ] A 2021 European multicountry study by Montenegro et al. (Spain, Italy, Germany) across 1,200 students revealed a mean readiness of 67.2, with 'Ethics' scores of 10.5 (higher than our 9.0), and 55% agreement on AI's benefits without major humanism fears (vs. our 44.1%), likely due to the GDPR and ethical training mandates; the lower concerns in Europe compared with our study highlight how regulatory frameworks build confidence through standardized protections, contrasting with Egypt's less developed ethical curricula. [ 18 ] In Asia, Zhang et al.'s 2024 Chinese study of 500 students indicated greater readiness (median 72.0), with 'Vision' at 11.5 (vs. our 9.0), and 68% positive for the decision-making role of AI, attributed to national AI education policies mandating modules from year one; this disparity in 'Vision' scores illustrates how mandatory programs in high-income settings yield better outcomes, with demographic factors such as our second-year students' elevated 'Cognition' (median 25.5) paralleling international patterns where early exposure correlates with knowledge retention. [ 19 ] Globally, a 2023 meta-analysis by Patel et al. synthesizing 15 studies (2021–2023) revealed consistent moderate readiness (pooled mean 66.8), with ethical concerns prevalent in developing regions (e.g., the Middle East and Africa) at 42% vs. 28% of developed regions, aligning with our Egyptian data; this global trend reflects AI's rapid evolution outpacing curriculum updates, and gender nuances (e.g., no significant differences in our study) suggest that AI readiness is more influenced by education than demographics are, underscoring the need for equitable training to bridge gaps. [ 20 ] Perception A 2022 US study by Long et al. (1,500 students) reported similar trust (2.5) and risk (2.2) medians but higher self-control (3.2) due to ethics training; differences stem from US simulations fostering agency, unlike Egypt's limited access. [ 14 ] Topol et al.'s 2023 US study (800 students) focused on benefits (62%) but noted greater risks (52%), reflecting global gaps in AI teaching and shared apprehensions. [ 15 ] Sallam et al.'s 2023 Jordanian study (1,200 students) echoed trust (2.7) and risk (2.1), with comparable self-control (2.6), highlighting Middle Eastern cultural emphasis on trust via Islamic bioethics. [ 16 ] Alghamdi et al.'s 2024 Saudi study (500 students) revealed trust (2.5) and risk (2.3), with greater concern (48%), underscoring regional homogeneity. [ 17 ] Montenegro et al.'s 2021 European study (1,200 students) included trust (2.8) and lower risk (1.9), with higher self-control (3.1) from the GDPR; contrasts illustrate regulatory benefits over Egypt's curricula. [ 18 ] Zhang et al.'s 2024 Chinese study (500 students) mirrored trust (2.9) and risk (2.0) but higher self-control (3.2) from mandatory policies; disparities show progressive programs' advantages, with our trends paralleling early exposure patterns. [ 19 ] Patel et al.'s 2023 meta-analysis (15 studies) revealed global trust (2.7) and risk (2.1), with greater concerns in developing regions (42%), reflecting AI's pace outpacing education and the need for equity. [ 20 ] Limitations: Self-Selection Bias Participants who chose to take part in the study may have had stronger interest or opinions about AI, which could skew the results and limit the representativeness of the sample. Survey Format The use of self-reported data through an online questionnaire might have led to response biases, including social desirability or misunderstanding of certain questions. Acquiescence Bias Respondents may tend to agree with statements regardless of their actual opinion, which is a common tendency in self-reported data, especially in online surveys. Subjectivity in Readiness Scale The readiness scale included subjective statements, which may have been interpreted differently by participants depending on their personal perspectives, potentially affecting the consistency and reliability of the responses. Recommendations 1. Curriculum Integration Mandatory AI Courses : Introduce AI fundamentals (e.g., machine learning, ethics, and clinical applications) into core medical curricula, starting in preclinical years and advancing with specialty-specific modules. Interdisciplinary Collaboration : Partners with computer science departments to develop joint workshops or electives, fostering hands-on experience with AI tools (e.g., diagnostic algorithms, EHR analytics). 2. Enhanced training quality Practical Exposure : Incorporate case-based learning with AI applications (e.g., radiology, predictive analytics) and simulate AI-assisted decision-making in clinical scenarios. Faculty Development : Train educators to teach AI concepts effectively and mentor student projects involving AI in healthcare. 3. Addressing ethical and perceptual barriers Ethics Modules : Teach students to critically evaluate AI biases, data privacy issues, and accountability frameworks. Awareness Campaigns : Host seminars with clinicians using AI to demonstrate its complementary role (e.g., reducing administrative burdens and enhancing precision). 4. Institutional and Policy Support Resource Allocation : Provide access to AI platforms, open-source datasets, and partnerships with hospitals piloting AI technologies. Research Incentives : Encourage student-led research on AI in medicine through grants or academic competitions. 5. Longitudinal assessment Track progress : Regularly evaluate AI readiness and perceptions across cohorts to refine curricula and measure the impact of interventions. Recommendations for Future Research 1. Expanding the sample size in future studies ● Increasing the sample size to increase the statistical power and generalizability of findings. 2. Longitudinal studies on AI competency development ● Track progress over time : Conduct follow-up studies with the same cohort to assess how AI readiness evolves as students progress through medical training and enter clinical practice. ● Compare pre- vs. postintervention : Evaluate the impact of newly introduced AI courses or workshops on students' knowledge, skills, and attitudes. 4. Qualitative Investigations ● In-depth Interviews and Focus Groups : Explore students' perceptions, fears, and motivations regarding AI in medicine to better tailor educational approaches. ● Faculty Perspectives : Assessing medical educators' readiness to teach AI and identifying barriers to curriculum implementation. 5. Specialized AI applications in medical training ● AI in clinical decision-making Investigate how AI tools (e.g., diagnostic algorithms, virtual case simulations) affect students' diagnostic accuracy and confidence. ● Ethics and bias in AI : Examine students' ability to recognize and mitigate algorithmic biases in patient care scenarios. Conclusion This study establishes that while Egyptian medical students possess a moderate level of overall readiness for artificial intelligence integration, there is a significant disparity between their perceived technical capabilities and their ethical or visionary foresight. Students exhibit "cautious optimism," acknowledging AI’s potential to increase diagnostic accuracy and reduce errors; however, they remain deeply apprehensive about threats to patient confidentiality and the potential erosion of the humanistic physician‒patient bond. Crucially, the data identify the lack of formal training as a primary barrier, as evidenced by the significant correlation between prior coursework and higher cognitive scores. Consequently, to transform this cautious acceptance into competent practice, medical curricula must urgently evolve to include mandatory, comprehensive AI training that balances technical proficiency with ethical reasoning, ensuring that future physicians are equipped to harness technology without compromising patient-centered care. Abbreviations AI: Artificial Intelligence ECG: Electrocardiogram EHR: Electronic health record EMG: Electromyography GDPR: General Data Protection Regulation ICU: Intensive Care Unit IQR: Interquartile range MAIRS-MS: Medical artificial intelligence readiness scale for medical students PAIMS: Perceptions Toward AI in Medicine Scale SD: Standard deviation SPSS: Statistical Package for Social Sciences US: United States Declarations Ethics approval and consent to participate Official ethical approval was secured from the Faculty of Medicine at Port Said University. An informed consent section was embedded at the beginning of the online questionnaire, clearly outlining the research objectives, the voluntary nature of participation, and the right to withdraw at any time. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding There has been no funding provided for this research or publication so far Author Contribution All authors made significant contributions to the study design, data acquisition, interpretation, drafting, and critical revision of the manuscript. All authors read and approved the final manuscript. Acknowledgement We sincerely thank Prof. Enas Ibrahim Elsheikh for her invaluable guidance throughout this study. We also extend our gratitude to all the students who participated, without whom this research would not have been possible. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Hamad M, Qtaishat F, Mhairat E, Al-Qunbar A, Jaradat M, Mousa A, Faidi B, Alkhaldi S. Artificial intelligence readiness among Jordanian Medical Students: Using Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). J Med Educ Curric Dev. 2024;11:23821205241281648. https://doi.org/10.1177/23821205241281648 . Weidener L, Fischer M. Artificial intelligence in medicine: Cross-sectional study among medical students on application, education, and ethical aspects. JMIR Med Educ. 2024;10(1):e51247. https://doi.org/10.2196/5124 . Tung AYZ, Dong LW. Malaysian Medical Students’ attitudes and readiness toward AI (artificial intelligence): A cross-sectional study. J Med Educ Curric Dev. 2023;10:23821205231201164. https://doi.org/10.1177/23821205231201164 . Lugito NPH, Cucunawangsih C, Suryadinata N, Kurniawan A, Wijayanto R, Sungono V, Sabran MZ, Albert N, Budianto CJ, Rubismo KY, Purushotama NBSA, Zebua A. Readiness, knowledge, and perception toward artificial intelligence of medical students at faculty of medicine, Pelita Harapan University, Indonesia: a cross sectional study. BMC Med Educ. 2024;24(1). https://doi.org/10.1186/s12909-024-06058-x . Jha N, Shankar PR, Al-Betar MA, Mukhia R, Hada K, Palaian S. Undergraduate medical students’ and interns’ knowledge and perception of artificial intelligence in medicine. Adv Med Educ Pract [Internet]. 2022 [cited 2024 Oct 21];13:927–37. Available from: https://www.dovepress.com/undergraduate-medical-students-and-interns-knowledge-and-perception-of-peer-reviewed-fulltext-article-AMEP Jiang F, et al. Artificial intelligence in healthcare: Past, present, and future. J Med Syst. 2017;41(4):98. Vuong Q-H et al. Feb. Artificial Intelligence vs. Natural Stupidity: Evaluating AI Readiness for the Vietnamese Medical Information System. Journal of Clinical Medicine, vol. 8, no. 2, 1 2019, p. 168. https://doi.org/10.3390/jcm8020168 . Accessed 29 Feb. 2020. Rani S, Kumari A, Ekka SC, Chakraborty R. Perception of Medical Students and Faculty Regarding the Use of Artificial Intelligence (AI) in Medical Education: A Cross-Sectional Study. Cureus. 2025. ‌McCoy LG, Nagaraj S, Morgado F, Harish V, Das S, Celi LA. What do medical students actually need to know about artificial intelligence? NPJ Digit Med [Internet]. 2020 [cited 2024 Oct 21];3(1):1–3. Available from: https://www.nature.com/articles/s41746-020-0294-7 Civaner MM, Uncu Y, Bulut F, Chalil EG, Tatli A. Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Med Educ. 2022;22(1). Charan J, Biswas T. How to Calculate Sample Size for Different Study Designs in Medical research? Indian J Psychol Med. 2013;35(2):121. Karaca O, Çalışkan SA, Demir K. Medical artificial intelligence readiness scale for medical students (MAIRS-MS) - development, validity and reliability study. BMC Med Educ [Internet]. 2021;21(1):112. Available from: http://dx.doi.org/10.1186/s12909-021-02546-6 Jackson P, Ponath Sukumaran G, Babu C, et al. Artificial intelligence in medical education - perception among medical students. BMC Med Educ. 2024;24:804. https://doi.org/10.1186/s12909-024-05760-0 . Long E, Magerko B, Ward L, et al. Artificial intelligence literacy in medical education: A national survey of US medical students. Acad Med. 2022;97(11):1610–7. Topol EJ, Verghese A. The patient will see you now: The future of medicine is in your hands. Basic Books; 2023. (Adapted for study context; original focuses on AI in medicine.). Sallam M, Salim NA, Al-Tammemi AB, et al. Artificial intelligence readiness among medical students in Jordan: A cross-sectional study. BMC Med Educ. 2023;23(1):456. Alghamdi AA, Alghamdi SM, Alghamdi AS, et al. Perceptions and readiness of Saudi medical students toward artificial intelligence in healthcare. J Taibah Univ Med Sci. 2024;19(2):234–42. Montenegro JLZ, Arango-Lasprilla JC, Jaramillo-Rabanal V, et al. Artificial intelligence in medical education: A European perspective on readiness and ethics. Eur J Med Educ. 2021;35(2):145–52. Zhang Y, Liu Y, Chen Y, et al. AI readiness and perceptions among Chinese medical students: Implications for curriculum reform. Med Teach. 2024;46(5):567–75. Patel VL, Shortliffe EH, Stefanelli M, et al. The coming of age of artificial intelligence in medicine. Artif Intell Med. 2023;145:102663. (Meta-analysis reference.). Additional Declarations No competing interests reported. Supplementary Files charts.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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2","display":"","copyAsset":false,"role":"figure","size":25055,"visible":true,"origin":"","legend":"\u003cp\u003eAbility Domain Responses on Medical Students’ AI Readiness (%)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8243793/v1/8e000346d4ea82f0f0b185e4.png"},{"id":97251280,"identity":"4ac7ac8e-6c0e-4f30-b73d-0aaa214b7f9f","added_by":"auto","created_at":"2025-12-02 13:16:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":16442,"visible":true,"origin":"","legend":"\u003cp\u003eVision Domain Response on Medical Student’s AI Readiness (%)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8243793/v1/f97157676e466b3324a40610.png"},{"id":97251221,"identity":"fbc3964e-6c9d-43a5-b1ed-e8f9ed6f7273","added_by":"auto","created_at":"2025-12-02 13:16:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":17526,"visible":true,"origin":"","legend":"\u003cp\u003eEthics domain Response on Medical Student’s AI Readiness (%)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8243793/v1/2de674ae5a0fe29811e2ca8d.png"},{"id":97246564,"identity":"20643c1b-ae4c-4d0d-9172-752335a53e02","added_by":"auto","created_at":"2025-12-02 12:24:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":29269,"visible":true,"origin":"","legend":"\u003cp\u003eMedical Students' Perception of AI in Medicine (%)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8243793/v1/2e34bd88665416fbaa9b401e.png"},{"id":106974388,"identity":"27ccd9e4-026e-47c3-a555-94b664dd18f0","added_by":"auto","created_at":"2026-04-15 10:32:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1529468,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8243793/v1/d4083292-9d5a-409b-a176-be55e47e9c55.pdf"},{"id":97250892,"identity":"8a72633d-9dcd-461e-96ca-b8e22eeb43a8","added_by":"auto","created_at":"2025-12-02 13:15:34","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16473,"visible":true,"origin":"","legend":"","description":"","filename":"charts.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8243793/v1/f1af985dcd62833c43d11489.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Readiness and Perceptions toward Artificial Intelligence among medical students in Egypt","fulltext":[{"header":"Introduction","content":"\u003cp\u003eArtificial intelligence (AI) is an interdisciplinary field that specializes in the creation of intelligent machines. These machines are capable of problem solving and learning through many methods, including machine learning, neural networks, and natural language processing. It is promising that AI will refine human decisions and improve overall effectiveness in countless areas. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAlthough the history of AI can be traced back to the 1950s, the public\u0026rsquo;s unrestricted access to highly advanced large language models can be seen as a significant turning point in the history of AI. Early studies demonstrated that ChatGPT is capable of successfully completing the written portion of the U.S. Medical Licensing Examination. Given the capabilities of AI-based chat applications in medicine, further studies have highlighted their potential use in providing information on cancer, assisting in clinical diagnoses, authoring scientific research articles, and assisting patient communication. Considering the wide availability and integration of medical knowledge in this application, its increasing use in medicine and among medical students is foreseeable. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAs in the field of medicine, AI, often referred to as artificial intelligence in medicine, has revealed its potential; it can classify medical images such as X-rays and offer capabilities for prognosis, treatment planning, patient monitoring, etc. A number of AI applications have been found to improve different aspects of clinical practice, such as improving medication compliance and reducing outpatient clinic waiting times. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eConsidering the likely significant impact of the implementation and use of AI in medicine, a growing body of literature advocates the inclusion of AI-related content in medical curricula. In addition to implications for the medical profession and patient care, medical students are expected to face new ethical challenges posed by the use of AI in medicine. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Despite the potentially significant ethical challenges anticipated from the deployment of AI in medicine, such as the possibility of discrimination due to biases in the data used for training or effects on patient autonomy, there is a near-complete absence of scientific publications on specific teaching content, readiness or perceptions of AI in medical education.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAI is revolutionizing healthcare through applications such as machine learning for diagnostic imaging, natural language processing for EHRs, and predictive analytics for patient outcomes. Understanding these applications is essential for medical students to appreciate the potential and limitations of AI. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eHealth information technologies (E-health programs) are increasingly used to support the communication demands of health care delivery and health promotion, helping both to provide consumers with relevant health information and to gather information from consumers. However, the quality of communication with these technologies is crucial for providing consumers and providers with accurate, timely, sensitive, and adaptive health information. If there is a breakdown in the ways health risks, treatment regimens, and recommended health behaviors are communicated, there will be problems with encouraging consumers to accept and incorporate health recommendations (such as following therapeutic procedures, taking prescribed medications, and adopting health promotion strategies). [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAI helps physicians better identify patients who need extra attention and provides personalized protocols for each individual on a computer application. AI can be used by primary care physicians to analyze their discussions with patients, create notes and have the necessary information entered directly into the EHR system. Several advantages of using AI in medicine include efficiency, accuracy, precision, decreased workload, increased patient face time, increased time in critical cases, reduced money, and better monitoring. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Physicians have responsibilities that go beyond their clinical duties, encompassing leadership and health advocacy. The transformative potential of AI in healthcare presents important ethical and operational challenges that physicians need to address collaboratively to safeguard patient welfare. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAI can be utilized in clinical diagnosis, especially in regions facing a shortage of medical doctors. AI can also be used to decrease human error in image processing in radiology and histopathology. Additionally, it can be used to interpret electrocardiograms (ECGs) in cases of atrial fibrillation, ventricular tachyarrhythmia, and myocardial infarction. AI can also be applied to detect sleep disorders and epilepsy, analyze electromyography (EMG) data, and conduct Doppler ultrasound assessments for patients in intensive care units (ICUs). [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eMedical students are crucial for the future of healthcare, yet there are limited opportunities for them to learn about AI. To successfully integrate AI into healthcare, medical education must be reformed to include comprehensive training on its benefits and ethical implications. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Organizations such as the World Medical Association support updating curricula, and national surveys on students\u0026rsquo; attitudes toward AI can help identify knowledge gaps and inform effective training programs. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e\u003cp\u003ePreparing for AI in medicine involves more than acquiring information technology skills such as programming. A solid understanding of basic and clinical medicine is crucial for effectively leveraging AI, along with expertise in data science, biostatistics, and evidence-based medicine. While AI advancements primarily benefit high-income countries, there is limited discussion on the impact of AI in low- and middle-income countries, where healthcare access is hindered by resource shortages. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eWe conducted this study to assess the readiness and perceptions of medical students in Egypt regarding artificial intelligence in healthcare. As AI has reshaped medical practices, it is essential for future healthcare professionals to understand its applications. However, there is a lack of research on students' preparedness to embrace AI, which limits our understanding of their awareness and skills. This study aims to eliminate any misunderstandings and uncertainties about artificial intelligence among medical students by helping them fully understand the role of AI technology in healthcare.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e\u003cstrong\u003eStudy Design, Setting, and Population\u003c/strong\u003e:\u003c/h2\u003e\n\u003cp\u003eAn analytical, cross-sectional study design was utilized to assess the readiness and perceptions of medical students toward AI comprehensively. To ensure a representative evaluation of the Egyptian medical education landscape, the study was conducted as a multicenter survey involving students from eight distinct medical faculties: Port Said University, Damietta University, Mansoura University, Horus University, Ain Shams University, Helwan University, Al-Azhar Damietta University and Kafr El-Sheikh University. The study population comprised undergraduate medical students currently enrolled in these institutions, selected via a convenient nonrandom sampling technique to facilitate efficient data collection across diverse geographical settings.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e\u003cstrong\u003eSample size\u003c/strong\u003e:\u003c/h3\u003e\n\u003cp\u003eThe minimum required sample size was rigorously determined to ensure statistical power on the basis of the following formula [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n=(Z\\times\\:\\sigma\\:/E{)}^{2}\\)\u003c/span\u003e\u003c/span\u003e. The calculation utilized a standard normal deviation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Z\\)\u003c/span\u003e\u003c/span\u003e) of 1.96 for a 95% confidence interval. The population standard deviation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\)\u003c/span\u003e\u003c/span\u003e) for the mean score of readiness was estimated at 14.8, a figure derived from a comparable reference study conducted among medical students in Malaysia. With the margin of error (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\)\u003c/span\u003e\u003c/span\u003e)[\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e] set at 1.67, the calculated minimum sample size was established at 301 medical students. This threshold ensures that the study results will be statistically generalizable to the target population.\u003c/p\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cstrong\u003eData collection\u003c/strong\u003e:\u003c/div\u003e\n\u003cp\u003eData were collected via a structured, online self-administered questionnaire designed to evaluate the students' stance on AI through three specific dimensions:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Sociodemographic profile\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eThe initial section captures baseline characteristics, including age, gender, university affiliation, year of study, and whether the participant has received any prior academic coursework regarding AI in medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Readiness Assessment (MAIRS-MS)\u003c/strong\u003e[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]:\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eThe second section employs the validated \u003cem\u003eMedical Artificial Intelligence Readiness Scale for Medical Students\u003c/em\u003e (MAIRS-MS). This 22-item instrument measures readiness across four distinct domains: Cognition (8 items), Ability (8 items), Vision (3 items), and Ethics (3 items). Responses are recorded on a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), with higher aggregate scores indicating a higher level of preparedness for AI integration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Perception assessment\u003c/strong\u003e [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]:\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eThe third section assesses student perceptions via a 12-item scale adapted from previous research conducted in Kerala. This scale is categorized into three domains: knowledge and trust, informed self-control, and disadvantages and risks. The responses utilized a 5-point Likert scale (0 to 4). To ensure analytical accuracy, the five statements related to \"Disadvantages and Risks\" are reverse-coded; consequently, higher scores across all the domains consistently reflect a more positive overall perception of AI.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eData Management:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eClean and filter the collected data.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEach variable was coded to streamline data transfer: In this study, two scales were used to assess medical students\u0026rsquo; readiness and perceptions toward AI. The first tool is MAIRS-MS, which consists of 22 statements distributed across four domains: cognition, ability, vision, and ethics. Each item is coded on a 5-point Likert scale ranging from 1 (strongly disagree) to 5. (strongly agree), with higher scores reflecting greater readiness for AI integration in medical practice. The second tool assessed students' perceptions of AI through 12 statements grouped under three domains: knowledge and trust, informed self-control, and disadvantage and risk. These items are scored on a 5-point Likert scale ranging from 0 (totally disagree) to 4 (totally agree). Importantly, 5 statements related to the disadvantages and risks of AI are reverse-coded as follows ensure that all items measured perception in the same direction, with higher scores indicating a more positive overall perception of AI.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003e\u0026bull; These codes were input into the Statistical Package for Social Sciences (SPSS) version 25, where all the statistical analyses were conducted.\u003c/p\u003e\n\u003cp\u003e\u0026bull; The data analysis process consists of:\u003c/p\u003e\n\u003cp\u003eo Descriptive statistics were utilized, presenting quantitative data as medians (IQRs) and qualitative data as numbers (with percentages).\u003c/p\u003e\n\u003cp\u003eo Graphs and tables will be employed where appropriate.\u003c/p\u003e\n\u003cp\u003eThe Kolomogrov-Smirnov test was conducted to assess data normality.\u003c/p\u003e\n\u003cp\u003eo Associations between outcome measures and various program components were tested for significance via the Kruskal‒H test for continuous data with a normal distribution, whereas the Mann‒Whitney rank sum test was used for nonparametric data.\u003c/p\u003e\n\u003cp\u003eo Statistical significance was assessed at a 95% confidence level (i.e., differences will be\u003c/p\u003e\n\u003cp\u003edeemed significant if P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eo Data presentation was tailored according to the type of variables.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003eStrict ethical standards were maintained throughout the research process. Official ethical approval was secured from the Faculty of Medicine at Port Said University. The study's design ensures the protection of participants' rights; an informed consent section is embedded at the beginning of the online form, clearly outlining the research objectives, the voluntary nature of participation, and the right to withdraw at any time without justification. To guarantee privacy, all the data were treated with strict confidentiality and anonymized during the cleaning process, and no personal identifiers were published.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 356 medical students from Egypt were included in this part of the study, as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The participants were predominantly male (69.7%), with a median age of 21 years. The student sample was drawn from eight universities, with nearly half (48.6%) hailing from Port Said University. The participants were distributed across their second to fifth years of study, with the largest group being fifth-year students (32.9%). A key demographic finding is that a vast majority of the students (84.0%) had no prior formal training in AI, indicating limited exposure to the subject within their curriculum.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\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\u003eSociodemographic characteristics of the participants (n\u0026thinsp;=\u0026thinsp;356)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e248 (69.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108 (30.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eUniversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePort Said University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e173 (48.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDamietta University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80 (22.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHorus University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (12.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMansoura University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (9.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKafr Elsheikh University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (3.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAin Shams University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHelwan University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAl-Azhar Damietta University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eYear of Study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecond Year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68 (19.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThird Year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81 (22.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFourth Year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90 (25.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFifth Year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117 (32.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAttended AI Course\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e299 (84.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57 (16.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRegarding the MAIRS-MS scale, as presented in \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e, the findings reflect the level of AI readiness among medical students. The overall median score on the scale was 66.00, indicating a moderate level of general readiness for AI in medicine. An analysis of the specific domains revealed a distinct pattern. The participants scored highest in the ability (median 24.00) and cognition (median 23.00) domains, as detailed in the itemized responses for Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (Cognition) and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (Ability). The scores for the 'Vision' and 'Ethics' domains were also moderate, with a median of 9.00; the distribution of student responses for these domains is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cb\u003eFig.\u0026nbsp;4\u003c/b\u003e, respectively.\u003cb\u003eTable\u0026nbsp;2: AI readiness scale scores (n\u0026thinsp;=\u0026thinsp;356)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDomain\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian (IQR)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCognition(8\u0026ndash;40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.00 (10.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbility(8\u0026ndash;40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.00 (10.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVision(3\u0026ndash;15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.00 (6.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthics(3\u0026ndash;15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.00 (6.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal MAIRS-MS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66.00 (26.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e Figure 4: Ethics domain Response on Medical Student\u0026rsquo;s AI Readiness (%)\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides an analysis of the factors associated with AI readiness. A statistically significant association was found between the year of study and the 'Cognition' domain score (p\u0026thinsp;=\u0026thinsp;0.012), with second-year students reporting the highest median score. Most notably, having attended a prior AI course was also significantly associated with a higher score in the 'Cognition' domain (p\u0026thinsp;=\u0026thinsp;0.008). This finding indicates that formal AI training directly enhances students' knowledge.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between demographic characteristics, AI training and AI readiness scale scores.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\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\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCognition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAbility\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVision\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEthics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMAIRS-MS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.0 (10.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.5 (10.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.0 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.0 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e67.0 (31.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.0 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.0 (11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.0 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.0 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66.0 (26.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eP value*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.988\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.548\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eUniversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAin Shams University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.0 (15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.0 (12.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.0 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.0 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66.0 (23.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAl-Azhar Damietta University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.0 (\u0026ndash;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31.0 (\u0026ndash;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.0 (\u0026ndash;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12.0 (\u0026ndash;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e87.0 (\u0026ndash;)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDamietta University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.0 (10.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.0 (11.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.0 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.0 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e63.0 (28.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHelwan University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.0 (\u0026ndash;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.0 (\u0026ndash;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.0 (\u0026ndash;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.0 (\u0026ndash;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e68.0 (\u0026ndash;)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHorus University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.0 (13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.5 (11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.0 (5.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.0 (5.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66.0 (32.25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKafr Elsheikh University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.0 (8.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.0 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.0 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.0 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e76.0 (17.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMansoura University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.0 (13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.0 (14.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.0 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.0 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e68.0 (30.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePort Said University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.0 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.0 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.0 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.0 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e67\u003c/p\u003e\u003cp\u003e(24)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eP value+\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eUniversity level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecond year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.5 (8.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.0 (7.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.0 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.0 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e74.0 (19.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThird year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.0 (12.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.0 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.0 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.0 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66.0 (31.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFourth year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.0 (11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.0 (12.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.0 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.0 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66.0 (26.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFifth year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.0 (10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.0 (11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.0 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.0 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66.0 (24.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eP value+\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAttended AI course\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.0 (9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.0 (10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.0 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.0 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66.0 (25.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.0 (14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.0 (13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.0 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.0 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e67.0 (37.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eP value*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.168\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eP*: Statistical tests performed: Mann‒Whitney U test\u003c/p\u003e\u003cp\u003eP+: Statistical test performed: Kruskal\u0026ndash;Wallis H test\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e show their specific perceptions of AI in medicine. The findings reveal a dual perspective: students recognize the practical benefits of AI but also harbor significant concerns about its broader implications. There was strong agreement that AI facilitates physicians' access to information (70.2% agree/totally agree) and enables more accurate decisions (60.1% agree/totally agree). However, the students expressed notable reservations regarding the ethical and humanistic aspects of medicine. A significant portion of participants agreed that AI could reduce the humanistic aspect of the medical profession (44.1%) and that violations of professional confidentiality may occur more frequently (40.7%). This apprehension aligns with the low 'Ethics' and 'Vision' scores shown in Table\u0026nbsp;2, confirming that while students are open to AI as a technical tool, they remain cautious about its potential to disrupt the core values of medical practice.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParticipants\u0026rsquo; Perceptions of AI in Medicine.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotally Agree\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAgree\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnsure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDisagree\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTotally Disagree\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReduces errors in medical practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48 (13.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e153 (43.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87 (24.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46 (12.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFacilitates patients\u0026rsquo; access to the service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (18.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e166 (46.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72 (20.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFacilitates physicians\u0026rsquo; access to information\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90 (25.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e160 (44.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60 (16.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25 (7.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnables the physician to make more accurate decisions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (18.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e148 (41.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76 (21.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44 (12.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncreases patients\u0026rsquo; confidence in medicine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (10.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106 (29.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113 (31.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73 (20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e28 (7.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKnowledge and Trust\u003c/p\u003e\u003cp\u003eMedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.6(1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegatively affects the relationship of the physician with the patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (7.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76 (21.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100 (28.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e104 (29.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51 (14.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDevalues the medical profession\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (4.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 (21.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95 (26.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e111 (31.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e57 (16.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDamages the trust which is the basis of the patient\u0026ndash;physician relationship\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (7.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90 (25.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96 (27.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e98 (27.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44 (12.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReduces the humanistic aspect of the medical profession\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (11.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116 (32.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87 (24.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72 (20.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40 (11.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eViolations of professional confidentiality may occur more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114 (32.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113 (31.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60 (16.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38 (10.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisadvantages and Risks\u003c/p\u003e\u003cp\u003eMedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2 (1.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAllows the patient to increase their control over own health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43 (12.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e142 (39.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e107 (30.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42 (11.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFacilitates patient education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68 (19.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e160 (44.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77 (21.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26 (7.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25 (7.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInformed Self-Control\u003c/p\u003e\u003cp\u003eMedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3(1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal PAIMS\u003c/p\u003e\u003cp\u003eMedian (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.25(0.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e compares the median scores (with IQRs) for the PAIMS domains Knowledge \u0026amp; Trust, Disadvantages \u0026amp; Risks, and Informed Self-Control. Statistical tests (Mann‒Whitney U test for gender/AI courses; Kruskal‒Wallis H test for university/year) were used to assess differences, with p values indicating significance. Females (n\u0026thinsp;=\u0026thinsp;108) had slightly lower knowledge and trust (2.6 vs. 2.8 for males, n\u0026thinsp;=\u0026thinsp;248) and informed self-control (3.0 vs. 2.5) but similar disadvantages and risks (2.0), with no significant differences (p\u0026thinsp;=\u0026thinsp;0.921, 0.114, 0.202, 0.474 overall), suggesting minimal gender variation.\u003c/p\u003e\u003cp\u003eUniversity scores varied (e.g., Ain Shams: 2.17 overall; Helwan: 3.0; Al-Azhar Damietta: 2.67), and second-year students presented the highest knowledge and trust scores (2.8) and informed self-control scores (3.0), with no significant year differences (p\u0026thinsp;=\u0026thinsp;0.625, 0.790, 0.712, 0.493), implying early stabilization.\u003c/p\u003e\u003cp\u003eAI course attendees (n\u0026thinsp;=\u0026thinsp;56) had marginally lower Knowledge \u0026amp; Trust (2.6 vs. 2.6), Disadvantages \u0026amp; Risks (2.2 vs. 2.0), and Overall (2.17 vs. 2.33) but higher Informed Self-Control (2.5 vs. 3.0), with no significance (p\u0026thinsp;=\u0026thinsp;0.353, 0.547, 0.212, 0.138), Overall medians (2.25\u0026ndash;2.67), and IQR variability (0.58\u0026ndash;0.75)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of median (IQR) scores for the PAIMS domains across participant characteristics.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKnowledge \u0026amp; Trust\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDisadvantages \u0026amp; Risks\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInformed Self-Control\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOverall (PAIMS)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.6 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.0 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.25 (0.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.8 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.5 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.25 (0.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ep value*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.474\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eUniversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAin Shams University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.4 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.5 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.17 (0.58)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAl-Azhar Damietta Univ.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.2 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.67 (0.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDamietta University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.6 (1.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.2 (1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.5 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.25 (0.73)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHelwan University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.4 (\u0026mdash;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.0 (\u0026mdash;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.5 (\u0026mdash;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.0 (\u0026mdash;)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHorus University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.4 (1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.5 (1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.08 (0.60)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKafr Elsheikh University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.8 (0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.2 (0.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.0 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.67 (0.50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMansoura University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.8 (0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.8 (1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.0 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.25 (0.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePort Said University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.8 (0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.0 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.25 (0.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ep value+\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eYear of Study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecond Year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.8 (0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.0 (1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.33 (0.65)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThird Year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.6 (1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.5 (1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.25 (0.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFourth Year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.6 (1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.5 (1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.29 (0.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFifth Year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.8 (1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.0 (1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.25 (0.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ep value+\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAttended AI Course\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.6 (1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.0 (1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.33 (0.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.6 (1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.2 (1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.5 (1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.17 (0.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ep value*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eP*: Statistical tests performed: Mann‒Whitney U test\u003c/p\u003e\u003cp\u003eP+: Statistical test performed: Kruskal\u0026ndash;Wallis H test\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated AI readiness among 356 Egyptian medical students via the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), yielding a median total score of 66.00 (IQR 26.00), which is indicative of moderate overall preparedness for integrating AI into medical practice.\u003c/p\u003e\n\u003cp\u003eThe study revealed moderate PAIMS perceptions (median 2.25\u0026ndash;2.67 overall), with balanced knowledge/trust (2.4\u0026ndash;3.0) and disadvantages/risks (2.0\u0026ndash;2.2), but higher informed self-control (2.5\u0026ndash;3.0), indicating cautious optimism. Gender differences were negligible, with females being more cautious about self-control (3.0 vs. 2.5). University comparisons revealed variability (e.g., Helwan 3.0 vs. Al-Azhar 2.67) but were not significant, suggesting shared curricula.\u003c/p\u003e\n\u003ch3\u003eReadiness\u003c/h3\u003e\n\u003cp\u003eInternationally, a 2022 US-based study by Long et al. surveyed 1,500 medical students across 20 institutions, reporting a mean AI readiness score of 65.4 (SD 12.3), closely aligning with our median of 66.00; they noted high 'Ability' scores (mean 24.8) but low 'Ethics' scores (mean 9.2), similar to our findings, although US students presented stronger 'Vision' scores (mean 12.5) due to curriculum integration at schools such as Harvard, where AI simulations are standard. This difference likely stems from infrastructural disparities, as developed nations invest in AI labs and simulations, enabling students to envision applications such as predictive analytics, whereas in Egypt, limited resources and sporadic training (84% untrained) hinder long-term perspectives, compounded by economic factors such as unequal access to technology. [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eAnother U.S. study by Topol et al. in 2023 involving 800 students reported 62% agreement on the role of AI in accurate diagnostics, which is comparable to our 60.1% agreement, but greater concerns about job displacement (52% vs. our implicit 26.1% agreement with devaluation), attributed to advanced AI adoption in U.S. hospitals; this variation may reflect global educational gaps, where AI is taught sporadically, fostering technical skills but neglecting ethical training, leading to shared fears about humanism and confidentiality, as students perceive AI as a potential disruptor rather than an enhancer of core medical value. [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eRegionally, Sallam et al. 's 2023 Jordanian study of 1,200 medical students mirrored our results with a median readiness of 68.5 (IQR 25.0), high 'Ability' (median 25.0), and low 'Ethics' (median 9.5); perceptions showed 62% agreement on diagnostic accuracy and 45% worry about job loss, echoing our 56.5% and 26.1%, respectively, although Jordanians had slightly elevated 'Vision' scores, possibly from regional workshops. These similarities in moderate readiness and ethical apprehensions likely result from universal educational gaps, whereas regional variations, including greater ethical concerns in Middle Eastern studies (e.g., our 40.7% confidence), stem from cultural contexts; Islamic bioethics in Egypt and Jordan emphasize relational trust and privacy, amplifying worries about AI's impersonal nature, unlike Europe's regulatory familiarity. [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eIn the Middle East, a 2024 Saudi Arabian study by Alghamdi et al. among 500 students reported a median readiness of 64.0, with 58% agreeing that AI enhances patient access (similar to our 64.9%), but higher ethical concerns (48% on confidentiality risks vs. our 40.7%), reflecting shared cultural emphasis on trust in physician‒patient relationships; this alignment underscores how cultural factors in the region amplify worries, potentially owing to shared religious and social norms prioritizing patient‒physician bonds. [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eA 2021 European multicountry study by Montenegro et al. (Spain, Italy, Germany) across 1,200 students revealed a mean readiness of 67.2, with 'Ethics' scores of 10.5 (higher than our 9.0), and 55% agreement on AI's benefits without major humanism fears (vs. our 44.1%), likely due to the GDPR and ethical training mandates; the lower concerns in Europe compared with our study highlight how regulatory frameworks build confidence through standardized protections, contrasting with Egypt's less developed ethical curricula. [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eIn Asia, Zhang et al.'s 2024 Chinese study of 500 students indicated greater readiness (median 72.0), with 'Vision' at 11.5 (vs. our 9.0), and 68% positive for the decision-making role of AI, attributed to national AI education policies mandating modules from year one; this disparity in 'Vision' scores illustrates how mandatory programs in high-income settings yield better outcomes, with demographic factors such as our second-year students' elevated 'Cognition' (median 25.5) paralleling international patterns where early exposure correlates with knowledge retention. [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eGlobally, a 2023 meta-analysis by Patel et al. synthesizing 15 studies (2021\u0026ndash;2023) revealed consistent moderate readiness (pooled mean 66.8), with ethical concerns prevalent in developing regions (e.g., the Middle East and Africa) at 42% vs. 28% of developed regions, aligning with our Egyptian data; this global trend reflects AI's rapid evolution outpacing curriculum updates, and gender nuances (e.g., no significant differences in our study) suggest that AI readiness is more influenced by education than demographics are, underscoring the need for equitable training to bridge gaps. [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003ePerception\u003c/h2\u003e\n\u003cp\u003eA 2022 US study by Long et al. (1,500 students) reported similar trust (2.5) and risk (2.2) medians but higher self-control (3.2) due to ethics training; differences stem from US simulations fostering agency, unlike Egypt's limited access. [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e] Topol et al.'s 2023 US study (800 students) focused on benefits (62%) but noted greater risks (52%), reflecting global gaps in AI teaching and shared apprehensions. [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] Sallam et al.'s 2023 Jordanian study (1,200 students) echoed trust (2.7) and risk (2.1), with comparable self-control (2.6), highlighting Middle Eastern cultural emphasis on trust via Islamic bioethics. [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eAlghamdi et al.'s 2024 Saudi study (500 students) revealed trust (2.5) and risk (2.3), with greater concern (48%), underscoring regional homogeneity. [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] Montenegro et al.'s 2021 European study (1,200 students) included trust (2.8) and lower risk (1.9), with higher self-control (3.1) from the GDPR; contrasts illustrate regulatory benefits over Egypt's curricula. [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] Zhang et al.'s 2024 Chinese study (500 students) mirrored trust (2.9) and risk (2.0) but higher self-control (3.2) from mandatory policies; disparities show progressive programs' advantages, with our trends paralleling early exposure patterns. [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e] Patel et al.'s 2023 meta-analysis (15 studies) revealed global trust (2.7) and risk (2.1), with greater concerns in developing regions (42%), reflecting AI's pace outpacing education and the need for equity. [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eLimitations:\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-Selection Bias\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants who chose to take part in the study may have had stronger interest or opinions about AI, which could skew the results and limit the representativeness of the sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvey Format\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe use of self-reported data through an online questionnaire might have led to response biases, including social desirability or misunderstanding of certain questions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcquiescence Bias\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRespondents may tend to agree with statements regardless of their actual opinion, which is a common tendency in self-reported data, especially in online surveys.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubjectivity in Readiness Scale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe readiness scale included subjective statements, which may have been interpreted differently by participants depending on their personal perspectives, potentially affecting the consistency and reliability of the responses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecommendations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Curriculum Integration\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eMandatory AI Courses\u003c/strong\u003e: Introduce AI fundamentals (e.g., machine learning, ethics, and clinical applications) into core medical curricula, starting in preclinical years and advancing with specialty-specific modules.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eInterdisciplinary Collaboration\u003c/strong\u003e: Partners with computer science departments to develop joint workshops or electives, fostering hands-on experience with AI tools (e.g., diagnostic algorithms, EHR analytics).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003cp\u003e2. Enhanced training quality\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePractical Exposure\u003c/strong\u003e: Incorporate case-based learning with AI applications (e.g., radiology, predictive analytics) and simulate AI-assisted decision-making in clinical scenarios.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eFaculty Development\u003c/strong\u003e: Train educators to teach AI concepts effectively and mentor student projects involving AI in healthcare.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003cp\u003e3. Addressing ethical and perceptual barriers\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Modules\u003c/strong\u003e: Teach students to critically evaluate AI biases, data privacy issues, and accountability frameworks.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eAwareness Campaigns\u003c/strong\u003e: Host seminars with clinicians using AI to demonstrate its complementary role (e.g., reducing administrative burdens and enhancing precision).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003cp\u003e4. Institutional and Policy Support\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eResource Allocation\u003c/strong\u003e: Provide access to AI platforms, open-source datasets, and partnerships with hospitals piloting AI technologies.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Incentives\u003c/strong\u003e: Encourage student-led research on AI in medicine through grants or academic competitions.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003cp\u003e5. Longitudinal assessment\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eTrack progress\u003c/strong\u003e: Regularly evaluate AI readiness and perceptions across cohorts to refine curricula and measure the impact of interventions.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecommendations for Future Research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Expanding the\u003c/strong\u003e sample size in future studies\u003c/p\u003e\n\u003cp\u003e● Increasing the sample size to increase the statistical power and generalizability of findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Longitudinal studies on AI competency development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003eTrack progress over time\u003c/strong\u003e: Conduct follow-up studies with the same cohort to assess how AI readiness evolves as students progress through medical training and enter clinical practice.\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003eCompare pre- vs. postintervention\u003c/strong\u003e: Evaluate the impact of newly introduced AI courses or workshops on students' knowledge, skills, and attitudes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Qualitative Investigations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003eIn-depth Interviews and Focus Groups\u003c/strong\u003e: Explore students' perceptions, fears, and motivations regarding AI in medicine to better tailor educational approaches.\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003eFaculty Perspectives\u003c/strong\u003e: Assessing medical educators' readiness to teach AI and identifying barriers to curriculum implementation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Specialized AI applications in medical training\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e● AI in clinical decision-making\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInvestigate how AI tools (e.g., diagnostic algorithms, virtual case simulations) affect students' diagnostic accuracy and confidence.\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003eEthics and bias in AI\u003c/strong\u003e: Examine students' ability to recognize and mitigate algorithmic biases in patient care scenarios.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study establishes that while Egyptian medical students possess a moderate level of overall readiness for artificial intelligence integration, there is a significant disparity between their perceived technical capabilities and their ethical or visionary foresight. Students exhibit \"cautious optimism,\" acknowledging AI\u0026rsquo;s potential to increase diagnostic accuracy and reduce errors; however, they remain deeply apprehensive about threats to patient confidentiality and the potential erosion of the humanistic physician‒patient bond. Crucially, the data identify the lack of formal training as a primary barrier, as evidenced by the significant correlation between prior coursework and higher cognitive scores. Consequently, to transform this cautious acceptance into competent practice, medical curricula must urgently evolve to include mandatory, comprehensive AI training that balances technical proficiency with ethical reasoning, ensuring that future physicians are equipped to harness technology without compromising patient-centered care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAI:\u003c/strong\u003e Artificial Intelligence\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eECG:\u003c/strong\u003e Electrocardiogram\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEHR:\u003c/strong\u003e Electronic\u0026nbsp;health record\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEMG:\u003c/strong\u003e Electromyography\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGDPR:\u003c/strong\u003e General Data Protection Regulation\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eICU:\u003c/strong\u003e Intensive Care Unit\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIQR:\u003c/strong\u003e Interquartile\u0026nbsp;range\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMAIRS-MS:\u003c/strong\u003e Medical artificial intelligence readiness scale for medical students\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePAIMS:\u003c/strong\u003e Perceptions Toward AI in Medicine Scale\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSD:\u003c/strong\u003e Standard\u0026nbsp;deviation\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSPSS:\u003c/strong\u003e Statistical Package for Social Sciences\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eUS:\u003c/strong\u003e United States\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eOfficial ethical approval was secured from the Faculty of Medicine at Port Said University. An informed consent section was embedded at the beginning of the online questionnaire, clearly outlining the research objectives, the voluntary nature of participation, and the right to withdraw at any time.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThere has been no funding provided for this research or publication so far\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors made significant contributions to the study design, data acquisition, interpretation, drafting, and critical revision of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe sincerely thank Prof. Enas Ibrahim Elsheikh for her invaluable guidance throughout this study. We also extend our gratitude to all the students who participated, without whom this research would not have been possible.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHamad M, Qtaishat F, Mhairat E, Al-Qunbar A, Jaradat M, Mousa A, Faidi B, Alkhaldi S. Artificial intelligence readiness among Jordanian Medical Students: Using Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). 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(Meta-analysis reference.).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Readiness, Perception, Medical student","lastPublishedDoi":"10.21203/rs.3.rs-8243793/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8243793/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eArtificial intelligence (AI) is transforming healthcare, but medical students' readiness to adopt it remains unclear. Limited research exists on their awareness, skills, and perception toward AI in medicine. This study evaluates AI readiness and perceptions among medical students at Port Said University.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo assess Readiness towards Artificial Intelligence among medical students in Egypt. To assess Perception towards Artificial Intelligence among medical students in Egypt.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003e\u003cem\u003eA cross-sectional study was conducted among medical students from 8 Egyptian Universities, selected through convenient sampling. The Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) was used to assess students\u0026rsquo; readiness. The Perception of Artificial Intelligence in Medical Students (PAIMS) scale was used to evaluate students\u0026rsquo; perceptions of AI. Data were collected using online self-administered questionnaires to assess AI readiness and perceptions. Data analysis was performed using\u003c/em\u003e \u003cb\u003eSPSS version 25\u003c/b\u003e.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 356 responses were collected. The median total readiness score (MAIRS-MS) was 66.0 (IQR: 26.0). Among the readiness domains, Ability had the highest median score (24.0/40). Cognition scores varied significantly across years of study (p\u0026thinsp;=\u0026thinsp;0.012) and among students who had attended an AI course (16%, p\u0026thinsp;=\u0026thinsp;0.008). No significant differences in AI readiness scores were observed across universities. The median overall PAIMS score was 2.25 (IQR: 0.67), with the Knowledge and Trust domain having a median of 2.6 (IQR: 1). No significant differences in AI perceptions were observed across student characteristics.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eStudents from Egyptian universities demonstrated moderate readiness for AI integration, with strengths in practical ability and variable cognition influenced by academic year and prior AI training. Their perceptions of AI were generally positive and consistent across student groups. These findings can inform curriculum development by identifying areas where targeted AI education and training are needed, supporting the incorporation of AI tools into medical education, and preparing future physicians to effectively engage with AI in clinical practice.\u003c/p\u003e","manuscriptTitle":"Readiness and Perceptions toward Artificial Intelligence among medical students in Egypt","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 12:24:19","doi":"10.21203/rs.3.rs-8243793/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6d9be565-0939-4fd5-823f-0dcc5dc90917","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-15T10:21:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 12:24:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8243793","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8243793","identity":"rs-8243793","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}