Attitudes and Readiness of Medical Students in Iraq towards Artificial Intelligence: A Cross-Sectional Study.

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Aya A. Al-Rubaye, Rasha A. Abdul-Qadir, Mustafa S. Issa, Ibrahim Saleem, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8169603/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Artificial Intelligence (AI) has emerged as a transformative force in healthcare, enhancing diagnostic precision, predicting outcomes, and optimizing administrative processes. In medical education, AI supports learning through simulation, automated assessments, and diagnostic training. However, successful implementation relies on clinicians’ and students’ readiness to adopt AI technologies. Objectives This study aims to assess the attitudes of Iraqi undergraduate medical students toward AI in healthcare and to evaluate their readiness across three key domains: ability, vision, and ethics for the integration of AI into medical education and clinical practice. Methods A cross-sectional study was conducted among 4th–6th-year medical students across Iraq from July 22 to September 1, 2025. Data were collected using a validated online questionnaire distributed via social media. The questionnaire consisted of three sections: (1) sociodemographic characteristics and AI exposure; (2) attitudes toward AI, using a modified version of the questionnaire developed by Pinto dos Santos et al.; and (3) readiness for AI integration, evaluated using a 14-item version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), covering the domains of ability, vision, and ethics. Cronbach’s alpha values of 0.856, 0.793, 0.825, and 0.880 for the respective domains and overall scale. Data were analyzed using SPSS v26. Mann–Whitney U test and Chi-square test were used to compare groups, with statistical significance set at p < 0.05. Results A total of 862 students responded (mean age 22.65 ± 1.62 years; 50.2% male). Nearly two-thirds used AI in both academic and personal contexts, and 55.7% considered themselves technologically skilled. Overall, > 60% agreed that AI will revolutionize diagnostic specialties and medicine and should be included in medical curricula. Readiness analysis revealed high agreement in the ability domain (> 70%) and moderate agreement for vision (> 5%), with strong ethical awareness (> 65%). Male and technologically skilled students demonstrated significantly more positive attitudes and overall readiness (p < 0.05). Conclusion Iraqi medical students exhibited generally positive attitudes and moderate readiness toward AI integration in healthcare. Technological competence significantly influenced both attitude and readiness levels. The study emphasizes the need for tailored educational programs to enhance preparedness for the future of AI-driven medicine. Artificial Intelligence Attitude Readiness Iraq Medical Education Medical Students Introduction The term “Artificial Intelligence” (AI) was first introduced by John McCarthy in 1956 at a conference specializing in this field ( 1 ) . Nevertheless, the concept of machines capable of imitating human behavior and engaging in genuine cognitive processes was first proposed by Alan Turing, who devised the Turing test to differentiate between humans and machines. From that point onward, computational capabilities have grown tremendously, enabling instantaneous calculations and real-time assessment of new data based on previously analyzed information ( 2 ) . AI is simply defined as “the incorporation of human intelligence into machines”. There are two subsets of AI; machine learning and deep learning. These techniques are used to train machines to mimic the human intelligence ( 3 ) . AI represents a field of computer science dedicated to developing systems that allow machines to carry out tasks typically requiring human intelligence. These tasks extended beyond elementary preprogrammed instructions and include decision making, analyzing visual and auditory data, and gaining knowledge from experience to make decisions ( 4 ) . The integration of AI into healthcare systems represents a transformative shift toward enhanced diagnostic accuracy, predicting patient outcomes, creating personalized treatment plans. AI can also optimize administrative tasks, and enhance patient flow ( 5 , 6 ) . In medical diagnostics, AI improves the accuracy and timing of image analysis in field of radiology and pathology ( 5 ) . Moreover, AI plays a key role in the automation of administrative tasks in healthcare system, monitor and manage patient health remotely, monitoring medications adherence, training and up-skilling healthcare personnel, and providing cost-effective healthcare ( 7 ) . To make clinicians comfortable with the role of AI in supporting clinical judgments, trust appears as a crucial element. Trust can be influenced by several human factors, including user education, prior encounters, individual predispositions, perceptions of automation, together with the characteristics and dependability of the technology itself ( 8 , 9 ) . AI, especially deep learning, has attracted significant attention in recent years within the fields of medical education and pathology ( 10 , 11 ) .Various areas of medical education have integrated AI, including generating exam questions, producing clinical case scripts, enhancing diagnostic and clinical skills among students and clinicians, serving as an interactive learning aid, and automating analytical tasks such as screening residency applications ( 12 ) . A number of elements may affect medical students' attitude, including their exposure to AI during medical education, their understanding benefits and limitations of AI, together with their future career goals ( 13 ) . In Iraq, research was limited and most available studies have primarily discussed general perceptions rather than practical readiness. A recent study conducted in Baghdad found that 38% of medical students reported low self-perceived knowledge of AI applications in medicine ( 14 ) , with the majority of medical students have never received training in AI. ( 14 , 15 ) . Therefore, this study was conducted to provide a broader national overview of Iraqi medical students’ attitudes and readiness toward AI. The study aims to assess the attitudes of Iraqi undergraduate medical students toward artificial intelligence (AI) in healthcare and to evaluate the readiness of medical students across the domains of ability, vision, and ethics for the integration of AI into medical education and clinical practice. Methods This is a descriptive cross-sectional study conducted in Iraq. We included undergraduate medical students from the 4th, 5th, and 6th years of medical education. The senior students receive more clinical training and exposure to different specialties in medicine, so they can meaningfully evaluate AI’s relevance in practice. We excluded medical students in their early years (1–3) because they often lack the clinical context to judge AI’s potential impact. The sample size was calculated using the following formula: n = Z 2 p(1 − p)​÷ d2 Where: Z = 1.96 for 95% confidence interval, p = expected proportion, d = margin of error (0.05). The expected response rate was 50%, thus the estimated sample size was 768 response. Data collection was continued until 862 valid responses were obtained. For this study, responses were collected using an online, self-administered questionnaire with closed-ended questions constructed by Google Forms. We restricted the collection to a single response from each participant to prevent multiple submissions from the same student. Prior to the main survey, a pilot test was conducted with 34 medical students to evaluate the clarity and non-response rate. The questionnaire was then disseminated to medical students across Iraq using social media platforms such as WhatsApp groups and Facebook. We employed a chain-referral sampling to enhance coverage across student groups along with reminder messages during the data collection period which extended from 22th July to 1st September 2025. The first section of the questionnaire encompasses sociodemographic aspects such as age, sex, academic year (4th, 5th, and 6th ), place of residence, and the college/university of enrollment. We also included two questions to assess their general exposure to AI. First, we asked whether the students have ever used any AI tools or applications (e.g., ChatGPT) in their studies or daily life. Second, if they consider themselves skilled in technology. Furthermore, we introduced a statement as follows: “AI is transforming specialties that rely on diagnostics, such as Radiology, Pathology, Dermatology (skin lesion analysis), Ophthalmology (retinal disease detection), Cardiology (ECG and echo analysis)” and asked if the students think that the use of AI would influence their choice of medical specialty or branch. The second section of the questionnaire was intended to assess the students’ attitudes towards AI, which was originally developed by Pinto Dos Santos et al. ( 16 ) This section included 10 statements, and the students were asked to indicate their level of agreement on a five-point Likert scale (strongly disagree, disagree, neutral, agree, or strongly agree). We replaced “radiology” in the original questionnaire with “diagnostic specialties” to make it more generalized. Additionally, we removed two statements from the original questionnaire: “In the foreseeable future, all physicians will be replaced” and “These developments make radiology more exciting to me.” The reliability of the scale, as assessed by Cronbach’s alpha, was 0.764, indicating an acceptable internal consistency. In the third section, we assessed the readiness of the medical students in terms of ability, vision, and ethics using a 14-item scale of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), a validated scale developed by O Karaca et al ( 17 ) . Each item was rated on a 5-point Likert scale. The original MAIRS-MS consists of 22 items across four domains (cognition, ability, vision, and ethics). The cognition domain, which primarily includes knowledge-based items related to data science, statistics, and AI-specific technical concepts, was excluded. This decision was made for two reasons: first, our study aimed to evaluate readiness in terms of skills, vision, and ethical considerations rather than factual knowledge; second, as medical students in our setting have not yet been exposed to formal AI-related training, inclusion of the cognition items would have been less relevant and potentially introduced measurement bias. Internal consistency reliability was assessed using Cronbach’s alpha and demonstrated satisfactory values: ability (0.856), vision (0.793), ethics (0.825), and overall scale (0.880). • Statistical Analysis The data were coded and analyzed using the Statistical Package for the Social Sciences (SPSS) version 26, developed by IBM Corporation, Armonk, New York, USA. Numeric variables were described as mean, standard deviation, minimum and maximum values. Categorical data were formulated as frequencies and percentages. The Mann–Whitney U test was employed to compare the differences in attitude and readiness statements toward AI between two independent groups: males vs. females and technologically skilled vs. non-skilled participants, since these variables were measured on ordinal (Likert-scale) responses. The Chi-square test was used to examine associations between categorical variables. A p-value of < 0.05 was considered statistically significant. Results In this study, 862 undergraduate medical students across Iraq responded to our Google form questionnaire with nearly equal participation from male and female students (50.2% and 49.8%, respectively). The mean age of the respondents was 22.65 ± 1.62 years, with those aged 21–23 years representing approximately 70% of the study population. A high participation was observed among 6th-year students, with 371 (43.0%) respondents, followed by 5th-year students 259 (30.0%) and 4th-year students 232 (26.9%). The majority of participants were from Baghdad (246, 28.5%), followed by Najaf (190, 22.0%). Participants were from various medical faculties, with the highest contribution was from University of Karbala (154, 17.9%), followed by Jabir ibn Hayyan Medical University (113, 13.1%), and Aliraqia University (111, 12.9%). Nearly equal numbers of participants were from the University of Baghdad (101, 11.7%), University of Basrah (99, 11.5%), and University of Kufa (99, 11.5%). Participants from other medical colleges (not previously listed) accounted for 185 (21.5%). Regarding AI usage, nearly two-thirds of participants reported using AI both in their medical studies and personal life (553, 64.2%), while 243 (28.2%) used it only in their studies, 35 (4.1%) only in personal life, and 31 (3.6%) had never used AI. More than half of the students considered themselves skilled in technology (480, 55.7%). Male participants were significantly more likely to consider themselves technologically skilled compared to female participants (58.5% vs. 41.5%, p < 0.05). When asked about the influence of AI on their choice of medical specialty, 375 (43.5%) responded 'Maybe', 261 (30.3%) 'No', and 226 (26.2%) 'Yes' as demonstrated in Table 1 . Table 1 Demographic data of medical students across several universities in Iraq (n = 862). Variable No. (%) Percentage Age (years) Mean ± SD 22.65 ± 1.62 Minimum-Maximum 18–30 = 24 213 24.7 Sex Male 433 50.2 Female 429 49.8 Academic year 4th year medical student 232 26.9 5th year medical student 259 30.0 6th year medical student 371 43.0 Place of Residence Baghdad 246 28.5 Najaf 190 22.0 Karbala 166 19.3 Basra 142 16.5 Other provinces 118 13.7 Medical Faculty College of Medicine, Aliraqia University 111 12.9 College of Medicine, University of Baghdad 101 11.7 College of Medicine, University of Basrah 99 11.5 College of Medicine, University of Karbala 154 17.9 Faculty of Medicine, Jabir Ibn Hayyan Medical University 113 13.1 Faculty of Medicine, University of Kufa 99 11.5 Other medical colleges 185 21.5 Have you ever used any artificial intelligence (AI) tools or applications (e.g., ChatGPT) in your studies or daily life? Yes, in my medical studies 243 28.2 Yes, in my personal life (non-medical) 35 4.1 Yes, both in my studies and personal life 553 64.2 No, I have never used AI 31 3.6 Do you consider yourself skilled in technology? Yes 480 55.7 No 382 44.3 Do you think the use of AI will influence your choice of medical specialty or branch? Yes 226 26.2 No 261 30.3 Maybe 375 43.5 No.: number SD: standard deviation For descriptive purposes, the five response categories were simplified, with ‘agree’ and ‘strongly agree’ referred to as agreement, and ‘strongly disagree’ and ‘disagree’ referred to as disagreement. However, statistical analyses (p-values) were conducted using the original five-point Likert scale. The attitude of Iraqi medical students towards AI was summarized in Table 2 . The majority of the study population (> 60%) agreed with the following statements: AI will revolutionize diagnostic specialties ; AI will revolutionize medicine in general ; these developments make medicine more exciting ; AI will never make the human physician replaceable ; AI will improve diagnostic specialties ; AI will improve medicine overall ; and AI should be included in medical training . The Mann–Whitney U test revealed a statistically significant difference between male and female students. Male participants expressed more positive attitudes (agreement) toward 6 out of 10 statements. Whereas, female participants demonstrated greater agreement with the statement ‘ AI will never make the human physician replaceable ’ with p = 0.001. No significant differences between males and females were observed with respect to these statements: ‘ The human (non-interventional) physician will be replaced in the near future ’ ‘ These developments frighten me ’ and ‘ These developments make medicine more exciting ’. On the other hand, participants who reported being competent in technology demonstrated statistically significant agreement with the following statements: ‘ AI will revolutionize diagnostic specialties ,’ ‘ The human radiologist/pathologist will be replaced in the near future ’ ‘ The human (non-interventional) physician will be replaced in the near future ’ and ‘ These developments make medicine more exciting in general ’ with p < 0.05. Table 3 . presents readiness towards AI among medical students across Iraq in terms of ability, vision, and ethics. Most participants agreed with AI ability statements, particularly regarding ‘ I find AI valuable for education, service, and research purposes ’ and ‘ I can utilize AI-based information combined with my professional knowledge ’, which revealed a more than 70% agreement rate. However, neutral responses were also common, representing approximately 30% of all responses in most statements. Significant sex differences were observed in several ability items, with males reporting higher agreement (p < 0.05). Moreover, technology-competent students consistently reported greater AI-related abilities compared with their technology-incompetent peers (p < 0.001). Regarding vision, overall about 50–58% agreement, which is lower compared to ability statements, with neutral responses being higher (31–36%). No significant sex differences were observed in all three statements of AI vision (p > 0.05). While technology-competent students consistently perceive themselves as more capable in identifying limitations, strengths, and opportunities of AI (p < 0.001). Table 3 For the ethical aspect of AI readiness, approximately two-thirds of the study population believed that they can use health data, act, and follow the ethical principles and legal regulations regarding the use of AI in healthcare. No statistically significant differences were observed among technology-competent and technology-incompetent students (p > 0.05). Male participants were significantly more likely to agree that they can follow the legal regulations regarding the use of AI technologies in healthcare (p = 0.029) Table 2 Attitude towards AI among medical students across Iraq (n = 862). Statement Strongly agree Agree Neutral Disagree Strongly disagree p-value (male vs. female)* p-value (Technology-competent vs. Technology-incompetent)* AI will revolutionize diagnostic specialties. 182 (21.1%) 355 (41.2%) 214 (24.8%) 86 (10.0%) 25 (2.9%) < 0.001 < 0.001 AI will revolutionize medicine in general. 180 (20.9%) 398 (46.2%) 183 (21.2%) 71 (8.2) 30 (3.5) < 0.001 0.231 The human radiologist/pathologist will be replaced in near future. 81 (9.4%) 214 (24.8%) 217 (25.2%) 252 (29.2%) 98 (11.4%) 0.001 < 0.001 The human (non-interventional) physician will be replaced in the near future. 63 (7.3%) 178 (20.6%) 199 (23.1%) 290 (33.6%) 132 (15.3%) 0.16 < 0.001 These developments frighten me. 98 (11.4%) 231 (26.8%) 262 (30.4%) 165 (19.1%) 106 (12.3%) 0.115 0.558 These developments make medicine in general more exciting to me. 182 (21.1%) 340 (39.4%) 256 (29.7%) 65 (7.5%) 19 (2.2%) 0.485 0.005 AI will never make the human physician replaceable. 290 (33.6%) 303 (35.2%) 171 (19.8%) 74 (8.6%) 24 (2.8%) 0.001 0.183 AI will improve diagnostic specialties. 263 (30.5%) 400 (46.4%) 147 (17.1%) 40 (4.6%) 12 (1.4%) 0.001 0.137 AI will improve medicine in general. 278 (32.3%) 418 (48.5%) 129 (15.0%) 30 (3.5%) 7 (0.8%) 0.021 0.283 AI should be part of medical training. 259 (30.0%) 336 (39.0%) 202 (23.4%) 50 (5.8%) 15 (1.7%) 0.011 0.101 *Mann-Whitney U test was used. Table 3 Readiness towards AI among medical students across Iraq in terms of ability, vision, and ethics (n = 862). Statement Strongly agree Agree Neutral Disagree Strongly disagree p-value (Male vs. Female)* p-value (Technology-competent vs. Technology-incompetent)* Ability I can utilize AI-based information combined with my professional knowledge. 206 (23.9) 472 (54.8) 163 (18.9) 19 (2.2) 2 (0.2) < 0.001 < 0.001 I can use AI technologies effectively and efficiently in healthcare delivery. 113 (13.1) 408 (47.3) 264 (30.6) 68 (7.9) 9 (1.0) < 0.001 < 0.001 I can use AI applications in accordance with their purpose. 141 (16.4) 434 (50.3) 241 (28.0) 42 (4.9) 4 (0.5) 0.015 < 0.001 I can access, evaluate, use, share, and create new knowledge using information and communication technologies. 133 (15.4) 408 (47.3) 258 (29.9) 55 (6.4) 8 (0.9) 0.201 0.014 I can explain how AI applications offer a solution to an appropriate problem in healthcare. 105 (12.2) 364 (42.2) 293 (34.0) 89 (10.3) 11 (1.3) < 0.001 < 0.001 I find AI valuable for education, service, and research purposes. 262 (30.4) 383 (44.4) 169 (19.6) 38 (4.4) 10 (1.2) 0.007 0.132 I can explain the applications of AI in healthcare services to the patient. 116 (13.5) 311 (36.1) 300 (34.8) 116 (13.5) 19 (2.2) 0.358 < 0.001 I can choose the proper application for AI to problems encountered in healthcare. 112 (13.0) 338 (39.2) 317 (36.8) 81 (9.4) 14 (1.6) < 0.001 < 0.001 Vision I can explain the limitations of AI technology. 134 (15.5) 351 (40.7) 296 (34.3) 69 (8.0) 134 (15.5) 0.091 < 0.001 I can explain the strengths and weaknesses of AI technology. 104 (12.1) 393 (45.6) 272 (31.6) 85 (9.9) 104 (12.1) 0.707 < 0.001 I can predict the opportunities and threats that AI technology can create. 109 (12.6) 327 (37.9) 315 (36.5) 101 (11.7) 109 (12.6) 0.051 < 0.001 Ethics I can use health data in accordance with legal and ethical norms. 178 (20.6) 396 (45.9) 231 (26.8) 44 (5.1) 178 (20.6) 0.064 0.076 I can act in accordance with ethical principles while using AI technologies 149 (17.3) 420 (48.7) 236 (27.4) 46 (5.3) 149 (17.3) 0.100 0.280 I can follow the legal regulations regarding the use of AI technologies in healthcare. 170 (19.7) 411 (47.7) 221 (25.6) 45 (5.2) 170 (19.7) 0.029 0.069 *Mann-Whitney U test was used. Discussion In the present study, Iraqi medical students expressed an overall positive attitude toward AI. Generally, male students and technology-competent individuals exhibited a more positive attitude across several aspects. The majority of our respondents (62–80%) believed that AI will revolutionize or improve medical practice, especially for specialties that relies on imagining such as radiology and pathology. With only 3.6% of the students reported no prior use of AI, the current generations of students are widely exposed to technological advancement which make it easier for them to adopt and accept the AI innovations in all aspects of life, including medicine. Comparable results were reported across several countries in the Middle East and North Africa (MENA) region. In a large multinational study that involved nine countries from the MENA region, it was reported that nearly 85% of the medical students thought that AI with revolutionize medicine and radiology in particular, with tech-savvy individual significantly held a more positive attitudes towards AI ( 18 ) . Likewise, studies from Syria, Saudi Arabia, and Jordan found similar results ( 19 – 22 ) . For instance, a study from Syria by Swed et al. (2022) reported a favorable attitude towards AI which was significantly more prevalent among male medical students and doctors. The majority of participants agreed that the application of AI is necessary in the medical field, and would aid in early diagnosis and assessment of disease severity ( 19 ) . A study from Jordan by Rjoop et al. (2025) included medical students and pathology trainees showed an overall favorable attitude towards AI. The study found that 81.4% of participants agreed that AI will revolutionize medicine in general and pathology in particular (79.2%) (20) . Two studies from Saudi Arabia further support this trend. Farooq et al. (2024) investigated attitudes toward AI in ophthalmology. The vast majority of the respondents believed that AI play a role in screening (96%), diagnosis (95.9%), and prevention (93%) of ophthalmic diseases ( 21 ) . Furthermore, Alabbad et al. (2025) reported that 81.8% of residents, interns, and medical students expressed their belief that AI aids in early diagnosis and disease assessment, and 79.7% considered it essential in medicine ( 22 ) . These trends of positive attitudes towards AI were not limited to the MENA region. Several studies from industrialized countries like the United Kingdom (UK) ( 23 ) , Germany ( 16 ) , and Malaysia ( 24 ) as well as from non-industrialized countries like Sudan ( 25 ) and Pakistan ( 26 ) have shown comparable findings. This reflects a widespread of optimism regarding the role of AI in healthcare. Our study has also identified gender-related differences in attitudes toward AI. Male students were more positive toward AI use and were also significantly more likely to perceive themselves as skilled in technology (p < 0.05). These disparities can be partly attributed to societal norms and cultural expectations, as in Iraq, like several other countries, males often have a greater affinity for technological engagement ( 27 ) . Furthermore, students who self-identified as technologically competent demonstrated significantly more favorable attitudes toward AI. This refers to the role of digital literacy as a contributing factor in shaping the attitude toward AI. The current study revealed that 40-48.9% of the study population expressed their disagreement with the possibility of replacement of human radiologists, pathologists, or non-interventional physicians in the near future. More than two-thirds of the students believed that “AI will never make the human physician replaceable” . However, 38.2% exhibited some fear regarding the recent AI advancement e.g. “These developments frighten me” . Moreover, a significant proportion of them indicated that these developments might affect their choice of medical specialty. This fear could be partly related to the anxiety about job security and the possibility of losing some jobs in the future. Additionally, it can be attributed to the ethical and confidential concerns related to the use of AI in healthcare .Medical student throughout several studies have expressed generally cautious reviews regarding the possibility of being replaced by AI in the future ( 20 , 28 , 29 ) . A study by Al Hadithy et al. (2023) included clinical-year medical students in Oman demonstrated some concerns about the implications of AI on future careers. More than 70% of subjects thought that AI would replace healthcare professionals within 11–25 years, particularly regarding diagnostic imaging. They also believed that physician job opportunities will be reduced (61.1%) with some specialties are vulnerable to these changes (77.8%) (29) . Similarly, nearly half of medical students in Pakistan agreed that AI would replace human medical professionals ( 26 ) . On the other hand, several studies showed an optimistic view of AI as a complementary tool that supports physicians rather than replacing them. A study in Jordan (2025) revealed that although more than half of the participants believed that AI could be used for automated diagnosis, a large majority of them disagreed that doctors in general (73.9%) or pathologists (63%) would be replaced in the foreseen future ( 20 ) . In Palestine (2024), nearly three-quarters of students disagreed that doctors would be completely or partially replaced by AI ( 28 ) . Clinical-year students showed higher odds of disagreements on radiologists being replaced by AI compared to pre-clinical students. This could reflect that the clinical exposure to real-world medical complexities could change the students’ perspective about AI in healthcare and can alleviate some misconceptions about AI ( 18 ) . These reviews convey that medical students across different countries are aware of the potential transformations of AI to the healthcare; however, they remain skeptical regarding the ability of AI to fully replace physicians in the near future. Their concerns regarding job security, particularly in diagnostic specialties, need attention and further discussion. Our study revealed that 69% of students expressed interest in learning about AI as part of their medical training. Likewise, two previous studies in Iraq the majority of medical students (> 70%) supported the idea of incorporating AI education in their curriculum, and it would be advantageous for their future careers ( 14 , 15 ) . Similar patterns have been observed in studies conducted in neighboring countries. Medical students and residents have demonstrated an increasing interest in integrating AI into medical education, residency, and fellowship training in Oman (79.6%), Saudi Arabia (75.9%), and Jordan (46.2%) ( 20 , 21 , 29 ) . These results indicate a pressing need to revise and update undergraduate and postgraduate medical curricula so that training aligns with the rapid advancement of AI tools. To the best of our knowledge, this is the first study in Iraq that assesses the readiness of university students to AI using a validated tool. A study by Murad ( 15 ) found that only 14.5% of students felt confident in their ability to use basic AI tools after graduation. This relied on a single self-perception question rather than a validated instrument. In contrast, our study found that a substantial proportion of respondents, ranging from approximately 50% to 80% expressed confidence in their abilities to adopt AI in healthcare. Technologically-competent individuals consistently perceived themselves as having a higher level of self-reported ability (p < 0.05). In the vision component of AI readiness, more than half of the medical students surveyed felt confident in their ability to understand AI's limitations, strengths, and weaknesses, as well as predicting the opportunities and threats AI can create in healthcare. This clearer vision was more evident among technology-competent respondents. Despite the data security and privacy concerns reported by the majority (84%) of Iraqi medical students in a study by Mahmood et al. ( 14 ) , the present study revealed that nearly two-thirds of the medical students demonstrated readiness to adopt ethical and legal principles of AI use in clinical practice; however, this did not significantly differ between technology-competent and non-competent students (p > 0.05). We reviewed several studies that used the MAIRS-MS questionnaire to assess medical students’ readiness to integrate AI in healthcare practices. We found broadly similar patterns across different countries. More than half of Jordanian medical students in a study by Hamad et al. exhibited readiness throughout the three domains of ability, vision, and ethics. Notably, students who received formal AI education or a prior course in AI were associated with better readiness scores ( 30 ) . A study by Baseer, S. et al. (2025) in Pakistan found that approximately two-thirds of medical and dental students showed readiness to AI in the three domains of ability, vision, and ethics, with more male students being engaged ( 31 ) . In Malaysia, Tung, A. et al (2023) found that while students reported comparable levels of readiness in the vision and ethics domains (> 50%), their confidence in the ability domain was notably lower (< 50% for most items) compared to our findings. This could partly reflect the rapid advancements and increasing familiarity with AI that have occurred over the past two years ( 24 ) . There was positive and negative attitudes towards AI, but positive attitudes outweigh negative attitudes. Medical students expressed high confidence in their readiness to adopt AI in healthcare. It is essential to understand that use of AI in medicine carries its own risks, for instance, ethical aspects and confidentiality of the patients, concerns about safety and efficacy of decisions, Our vision aligns with expert recommendations from other countries ( 17 , 24 ) . We believe that incorporation of AI in medical curriculum needs to highlight understanding the basics of AI, how to deal with medical datasets, AI-driven medical applications and scenarios, emphasis on AI's ethical and legal domains, and limitations and drawbacks of AI. Moreover, we recommend that AI training is provided to younger students to adopt AI innovations and gain confidence in interacting with technology. Incorporating AI in the medical curriculum for undergraduate medical student helps to address their fears and concerns. Ensuring that they realize that AI is likely to support not replace doctors and the use of AI still requires professional supervision and monitoring, as it cannot ethically be considered responsible ( 24 ) . We acknowledge several limitations in our study. First, we relied on self-reported data, which could be subjected to social desirability and selective memory of participants. Second, a cross-sectional study design cannot establish causality. Third, the study did not capture all medical colleges across Iraq. We recommend that future longitudinal study designs, including more colleges particularity in north and west Iraq to verify the generalizability of the results. Conclusions To the best of our knowledge, this is the first study in Iraq to assess medical students’ attitudes and readiness toward AI using validated tools. We found that a predominantly favorable attitudes towards AI with interest in integrating AI into the medical curriculum. Medical students demonstrated confidence in their ability to work with AI, a clear vision of its limitations and weaknesses, and their willingness to follow the ethical principles and legal regulations. These findings highlight the need to incorporate structured AI training into medical curricula to enhance students’ ability, vision, and ethical preparedness for the future of AI-driven medicine. Abbreviations AI Artificial Intelligence ECG Electrocardiogram IBM International Business Machines Corporation MAIRS-MS Medical Artificial Intelligence Readiness Scale for Medical Students MENA Middle East and North Africa SD Standard Deviation SPSS Statistical Package for the Social Sciences UK United Kingdom USA United States of America Declarations Acknowledgements We would like to thank Omar A. Al-Saffar for his assistance in data collection for this study and all the medical students who participated in this study. Author contributions A.A.A. served as the principal investigator, leading study conception, methodology, supervision of data collection, performing data analysis, preparing tables, and drafting the manuscript. R.A.A. and M.S.I. contributed to drafting the introduction and critically reviewing manuscript. M.S.I., I.S., M.A.T.A., H.A., A.Y.K., and H.A.H. contributed to data collection, survey distribution, and literature synthesis. All authors read and approved the final version of the manuscript. Funding None. Data availability The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate All procedures in this study were conducted in accordance with the ethical standards of the institutional and national research committees and the Declaration of Helsinki (1964), as revised in 2013. Ethical approval was granted by the Ministry of Higher Education, University of Basrah, College of Medicine. The participants were fully informed about the research and provided their consent. The collected data were used exclusively for the purpose of the study. Generative AI and AI-assisted technologies in the writing process ChatGPT (OpenAI, GPT-5) was used in a limited manner under the supervision of the authors to improve phrasing, grammatical accuracy, and formatting consistency. The AI tool was not involved in study design, data collection, analysis, interpretation, or drawing scientific conclusions. The authors take full responsibility for the integrity and accuracy of the content. Conflict of Interests The authors have no conflict of interests to declare. Consent for Publication Not applicable. Standards of Reporting This cross-sectional study involving human participants adhered to the STROBE guidelines. 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Swed S, Alibrahim H, Elkalagi NKH, Nasif MN, Rais MA, Nashwan AJ, et al. Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Syria: A cross-sectional online survey. Front Artif Intell. 2022;5–2022. https://doi.org/10.3389/frai.2022.1011524 . Rjoop A, Al-Qudah M, Alkhasawneh R, Bataineh N, Abdaljaleel M, Rjoub MA, et al. Awareness and Attitude Toward Artificial Intelligence Among Medical Students and Pathology Trainees: Survey Study. JMIR Med Educ. 2025;11:e62669. https://doi.org/10.2196/62669 . Faroog Z, Dirar QSE, Zaidi ARZ, Khan MS, Mahamud G, Ambia SR, et al. Knowledge and attitude of medical students towards artificial intelligence in ophthalmology in Riyadh, Saudi Arabia: a cross-sectional study. Ann Med Surg (Lond). 2024;86(8):4377–83. https://doi.org/10.1097/ms9.0000000000002238 . Alabbad FA, Almeneessier AS, Alshalan MH, Aljarba MN. Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Saudi Arabia. J Family Med Prim Care. 2025;14(4):1459–64. https://doi.org/10.4103/jfmpc.jfmpc_1812_24 . Sit C, Srinivasan R, Amlani A, Muthuswamy K, Azam A, Monzon L, et al. Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights into Imaging. 2020;11(1):14. https://doi.org/10.1186/s13244-019-0830-7 . 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 . Jaber Amin MH, Mohamed Elhassan Elmahi MA, Abdelmonim GA, Fadlalmoula GA, Jaber Amin JH, Khalid Alrabee NH, et al. Knowledge, attitude, and practice of artificial intelligence among medical students in Sudan: a cross-sectional study. Ann Med Surg (Lond). 2024;86(7):3917–23. https://doi.org/10.1097/ms9.0000000000002070 . Sami A, Tanveer F, Sajwani K, Kiran N, Javed MA, Ozsahin DU, et al. Medical students' attitudes toward AI in education: perception, effectiveness, and its credibility. BMC Med Educ. 2025;25(1):82. https://doi.org/10.1186/s12909-025-06704-y . Choudhary S, Kandel L. Gender Differences in Affinity Toward Technology Among Undergraduate Management Students: A Statistical Analysis. NPRC J Multidisciplinary Res. 2025;2:81–96. https://doi.org/10.3126/nprcjmr.v2i3.76959 . Jebreen K, Radwan E, Kammoun-Rebai W, Alattar E, Radwan A, Safi W, et al. Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine. BMC Med Educ. 2024;24(1):507. https://doi.org/10.1186/s12909-024-05465-4 . Al Hadithy ZA, Al Lawati A, Al-Zadjali R, Al Sinawi H, Knowledge. Attitudes, and Perceptions of Artificial Intelligence in Healthcare Among Medical Students at Sultan Qaboos University. Cureus. 2023;15(9):e44887. https://doi.org/10.7759/cureus.44887 . Hamad M, Qtaishat F, Mhairat E, Al-Qunbar A, Jaradat M, Mousa A, et al. 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 . Baseer S, Jamil B, Khan SA, Khan M, Syed A, Ali L. Readiness towards artificial intelligence among medical and dental undergraduate students in Peshawar, Pakistan: a cross-sectional survey. BMC Med Educ. 2025;25(1):632. https://doi.org/10.1186/s12909-025-06911-7 Additional Declarations No competing interests reported. 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Nevertheless, the concept of machines capable of imitating human behavior and engaging in genuine cognitive processes was first proposed by Alan Turing, who devised the Turing test to differentiate between humans and machines. From that point onward, computational capabilities have grown tremendously, enabling instantaneous calculations and real-time assessment of new data based on previously analyzed information \u003csup\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAI is simply defined as \u0026ldquo;the incorporation of human intelligence into machines\u0026rdquo;. There are two subsets of AI; machine learning and deep learning. These techniques are used to train machines to mimic the human intelligence\u003csup\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/sup\u003e. AI represents a field of computer science dedicated to developing systems that allow machines to carry out tasks typically requiring human intelligence. These tasks extended beyond elementary preprogrammed instructions and include decision making, analyzing visual and auditory data, and gaining knowledge from experience to make decisions\u003csup\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe integration of AI into healthcare systems represents a transformative shift toward enhanced diagnostic accuracy, predicting patient outcomes, creating personalized treatment plans. AI can also optimize administrative tasks, and enhance patient flow \u003csup\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/sup\u003e. In medical diagnostics, AI improves the accuracy and timing of image analysis in field of radiology and pathology\u003csup\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/sup\u003e. Moreover, AI plays a key role in the automation of administrative tasks in healthcare system, monitor and manage patient health remotely, monitoring medications adherence, training and up-skilling healthcare personnel, and providing cost-effective healthcare \u003csup\u003e(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo make clinicians comfortable with the role of AI in supporting clinical judgments, trust appears as a crucial element. Trust can be influenced by several human factors, including user education, prior encounters, individual predispositions, perceptions of automation, together with the characteristics and dependability of the technology itself \u003csup\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAI, especially deep learning, has attracted significant attention in recent years within the fields of medical education and pathology \u003csup\u003e(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/sup\u003e.Various areas of medical education have integrated AI, including generating exam questions, producing clinical case scripts, enhancing diagnostic and clinical skills among students and clinicians, serving as an interactive learning aid, and automating analytical tasks such as screening residency applications \u003csup\u003e(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA number of elements may affect medical students' attitude, including their exposure to AI during medical education, their understanding benefits and limitations of AI, together with their future career goals \u003csup\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn Iraq, research was limited and most available studies have primarily discussed general perceptions rather than practical readiness. A recent study conducted in Baghdad found that 38% of medical students reported low self-perceived knowledge of AI applications in medicine \u003csup\u003e(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/sup\u003e, with the majority of medical students have never received training in AI. \u003csup\u003e(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/sup\u003e. Therefore, this study was conducted to provide a broader national overview of Iraqi medical students\u0026rsquo; attitudes and readiness toward AI.\u003c/p\u003e\u003cp\u003eThe study aims to assess the attitudes of Iraqi undergraduate medical students toward artificial intelligence (AI) in healthcare and to evaluate the readiness of medical students across the domains of ability, vision, and ethics for the integration of AI into medical education and clinical practice.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis is a descriptive cross-sectional study conducted in Iraq. We included undergraduate medical students from the 4th, 5th, and 6th years of medical education. The senior students receive more clinical training and exposure to different specialties in medicine, so they can meaningfully evaluate AI\u0026rsquo;s relevance in practice. We excluded medical students in their early years (1\u0026ndash;3) because they often lack the clinical context to judge AI\u0026rsquo;s potential impact.\u003c/p\u003e\u003cp\u003eThe sample size was calculated using the following formula: n\u0026thinsp;=\u0026thinsp;Z\u003csup\u003e2\u003c/sup\u003ep(1\u0026thinsp;\u0026minus;\u0026thinsp;p)​\u0026divide; d2\u003c/p\u003e\u003cp\u003eWhere: Z\u0026thinsp;=\u0026thinsp;1.96 for 95% confidence interval, p\u0026thinsp;=\u0026thinsp;expected proportion, d\u0026thinsp;=\u0026thinsp;margin of error (0.05). The expected response rate was 50%, thus the estimated sample size was 768 response. Data collection was continued until 862 valid responses were obtained.\u003c/p\u003e\u003cp\u003eFor this study, responses were collected using an online, self-administered questionnaire with closed-ended questions constructed by Google Forms. We restricted the collection to a single response from each participant to prevent multiple submissions from the same student. Prior to the main survey, a pilot test was conducted with 34 medical students to evaluate the clarity and non-response rate. The questionnaire was then disseminated to medical students across Iraq using social media platforms such as WhatsApp groups and Facebook. We employed a chain-referral sampling to enhance coverage across student groups along with reminder messages during the data collection period which extended from 22th July to 1st September 2025.\u003c/p\u003e\u003cp\u003eThe first section of the questionnaire encompasses sociodemographic aspects such as age, sex, academic year (4th, 5th, and 6th ), place of residence, and the college/university of enrollment. We also included two questions to assess their general exposure to AI. First, we asked whether the students have ever used any AI tools or applications (e.g., ChatGPT) in their studies or daily life. Second, if they consider themselves skilled in technology. Furthermore, we introduced a statement as follows: \u003cem\u003e\u0026ldquo;AI is transforming specialties that rely on diagnostics, such as Radiology, Pathology, Dermatology (skin lesion analysis), Ophthalmology (retinal disease detection), Cardiology (ECG and echo analysis)\u0026rdquo;\u003c/em\u003e and asked if the students think that the use of AI would influence their choice of medical specialty or branch.\u003c/p\u003e\u003cp\u003eThe second section of the questionnaire was intended to assess the students\u0026rsquo; attitudes towards AI, which was originally developed by Pinto Dos Santos et al.\u003csup\u003e(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/sup\u003e This section included 10 statements, and the students were asked to indicate their level of agreement on a five-point Likert scale (strongly disagree, disagree, neutral, agree, or strongly agree). We replaced \u0026ldquo;radiology\u0026rdquo; in the original questionnaire with \u0026ldquo;diagnostic specialties\u0026rdquo; to make it more generalized. Additionally, we removed two statements from the original questionnaire: \u003cem\u003e\u0026ldquo;In the foreseeable future, all physicians will be replaced\u0026rdquo;\u003c/em\u003e and \u003cem\u003e\u0026ldquo;These developments make radiology more exciting to me.\u0026rdquo;\u003c/em\u003e The reliability of the scale, as assessed by Cronbach\u0026rsquo;s alpha, was 0.764, indicating an acceptable internal consistency.\u003c/p\u003e\u003cp\u003eIn the third section, we assessed the readiness of the medical students in terms of ability, vision, and ethics using a 14-item scale of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), a validated scale developed by O Karaca et al\u003csup\u003e(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/sup\u003e. Each item was rated on a 5-point Likert scale. The original MAIRS-MS consists of 22 items across four domains (cognition, ability, vision, and ethics). The cognition domain, which primarily includes knowledge-based items related to data science, statistics, and AI-specific technical concepts, was excluded. This decision was made for two reasons: first, our study aimed to evaluate readiness in terms of skills, vision, and ethical considerations rather than factual knowledge; second, as medical students in our setting have not yet been exposed to formal AI-related training, inclusion of the cognition items would have been less relevant and potentially introduced measurement bias. Internal consistency reliability was assessed using Cronbach\u0026rsquo;s alpha and demonstrated satisfactory values: ability (0.856), vision (0.793), ethics (0.825), and overall scale (0.880).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e\u0026bull; Statistical Analysis\u003c/h2\u003e\u003cp\u003eThe data were coded and analyzed using the Statistical Package for the Social Sciences (SPSS) version 26, developed by IBM Corporation, Armonk, New York, USA. Numeric variables were described as mean, standard deviation, minimum and maximum values. Categorical data were formulated as frequencies and percentages. The Mann\u0026ndash;Whitney U test was employed to compare the differences in attitude and readiness statements toward AI between two independent groups: males vs. females and technologically skilled vs. non-skilled participants, since these variables were measured on ordinal (Likert-scale) responses. The Chi-square test was used to examine associations between categorical variables. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn this study, 862 undergraduate medical students across Iraq responded to our Google form questionnaire with nearly equal participation from male and female students (50.2% and 49.8%, respectively). The mean age of the respondents was 22.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62 years, with those aged 21\u0026ndash;23 years representing approximately 70% of the study population. A high participation was observed among 6th-year students, with 371 (43.0%) respondents, followed by 5th-year students 259 (30.0%) and 4th-year students 232 (26.9%). The majority of participants were from Baghdad (246, 28.5%), followed by Najaf (190, 22.0%).\u003c/p\u003e\u003cp\u003e Participants were from various medical faculties, with the highest contribution was from University of Karbala (154, 17.9%), followed by Jabir ibn Hayyan Medical University (113, 13.1%), and Aliraqia University (111, 12.9%). Nearly equal numbers of participants were from the University of Baghdad (101, 11.7%), University of Basrah (99, 11.5%), and University of Kufa (99, 11.5%). Participants from other medical colleges (not previously listed) accounted for 185 (21.5%).\u003c/p\u003e\u003cp\u003eRegarding AI usage, nearly two-thirds of participants reported using AI both in their medical studies and personal life (553, 64.2%), while 243 (28.2%) used it only in their studies, 35 (4.1%) only in personal life, and 31 (3.6%) had never used AI. More than half of the students considered themselves skilled in technology (480, 55.7%). Male participants were significantly more likely to consider themselves technologically skilled compared to female participants (58.5% vs. 41.5%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). When asked about the influence of AI on their choice of medical specialty, 375 (43.5%) responded 'Maybe', 261 (30.3%) 'No', and 226 (26.2%) 'Yes' as demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eDemographic data of medical students across several universities in Iraq (n\u0026thinsp;=\u0026thinsp;862).\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\u003cp\u003eNo.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(%) Percentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimum-Maximum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;= 20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21\u0026ndash;23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;= 24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAcademic year\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4th year medical student\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5th year medical student\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6th year medical student\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlace of Residence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaghdad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNajaf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKarbala\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasra\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther provinces\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedical Faculty\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege of Medicine, Aliraqia University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege of Medicine, University of Baghdad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege of Medicine, University of Basrah\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege of Medicine, University of Karbala\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFaculty of Medicine, Jabir Ibn Hayyan Medical University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFaculty of Medicine, University of Kufa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther medical colleges\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHave you ever used any artificial intelligence (AI) tools or applications (e.g., ChatGPT) in your studies or daily life?\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes, in my medical studies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes, in my personal life (non-medical)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes, both in my studies and personal life\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo, I have never used AI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDo you consider yourself skilled in technology?\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDo you think the use of AI will influence your choice of medical specialty or branch?\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaybe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eNo.: number\u003c/p\u003e\u003cp\u003eSD: standard deviation\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 For descriptive purposes, the five response categories were simplified, with \u0026lsquo;agree\u0026rsquo; and \u0026lsquo;strongly agree\u0026rsquo; referred to as agreement, and \u0026lsquo;strongly disagree\u0026rsquo; and \u0026lsquo;disagree\u0026rsquo; referred to as disagreement. However, statistical analyses (p-values) were conducted using the original five-point Likert scale.\u003c/p\u003e\u003cp\u003eThe attitude of Iraqi medical students towards AI was summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The majority of the study population (\u0026gt;\u0026thinsp;60%) agreed with the following statements: \u003cem\u003eAI will revolutionize diagnostic specialties\u003c/em\u003e; \u003cem\u003eAI will revolutionize medicine in general\u003c/em\u003e; \u003cem\u003ethese developments make medicine more exciting\u003c/em\u003e; \u003cem\u003eAI will never make the human physician replaceable\u003c/em\u003e; \u003cem\u003eAI will improve diagnostic specialties\u003c/em\u003e; \u003cem\u003eAI will improve medicine overall\u003c/em\u003e; and \u003cem\u003eAI should be included in medical training\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eThe Mann\u0026ndash;Whitney U test revealed a statistically significant difference between male and female students. Male participants expressed more positive attitudes (agreement) toward 6 out of 10 statements. Whereas, female participants demonstrated greater agreement with the statement \u0026lsquo;\u003cem\u003eAI will never make the human physician replaceable\u003c/em\u003e\u0026rsquo; with p\u0026thinsp;=\u0026thinsp;0.001. No significant differences between males and females were observed with respect to these statements: \u0026lsquo;\u003cem\u003eThe human (non-interventional) physician will be replaced in the near future\u003c/em\u003e\u0026rsquo; \u0026lsquo;\u003cem\u003eThese developments frighten me\u003c/em\u003e\u0026rsquo; and \u0026lsquo;\u003cem\u003eThese developments make medicine more exciting\u003c/em\u003e\u0026rsquo;.\u003c/p\u003e\u003cp\u003eOn the other hand, participants who reported being competent in technology demonstrated statistically significant agreement with the following statements: \u0026lsquo;\u003cem\u003eAI will revolutionize diagnostic specialties\u003c/em\u003e,\u0026rsquo; \u0026lsquo;\u003cem\u003eThe human radiologist/pathologist will be replaced in the near future\u003c/em\u003e\u0026rsquo; \u0026lsquo;\u003cem\u003eThe human (non-interventional) physician will be replaced in the near future\u003c/em\u003e\u0026rsquo; and \u0026lsquo;\u003cem\u003eThese developments make medicine more exciting in general\u003c/em\u003e\u0026rsquo; with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. presents readiness towards AI among medical students across Iraq in terms of ability, vision, and ethics. Most participants agreed with AI ability statements, particularly regarding \u0026lsquo;\u003cem\u003eI find AI valuable for education, service, and research purposes\u003c/em\u003e\u0026rsquo; and \u0026lsquo;\u003cem\u003eI can utilize AI-based information combined with my professional knowledge\u003c/em\u003e\u0026rsquo;, which revealed a more than 70% agreement rate. However, neutral responses were also common, representing approximately 30% of all responses in most statements. Significant sex differences were observed in several ability items, with males reporting higher agreement (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, technology-competent students consistently reported greater AI-related abilities compared with their technology-incompetent peers (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eRegarding vision, overall about 50\u0026ndash;58% agreement, which is lower compared to ability statements, with neutral responses being higher (31\u0026ndash;36%). No significant sex differences were observed in all three statements of AI vision (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). While technology-competent students consistently perceive themselves as more capable in identifying limitations, strengths, and opportunities of AI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e\u003cp\u003e For the ethical aspect of AI readiness, approximately two-thirds of the study population believed that they can use health data, act, and follow the ethical principles and legal regulations regarding the use of AI in healthcare. No statistically significant differences were observed among technology-competent and technology-incompetent students (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Male participants were significantly more likely to agree that they can follow the legal regulations regarding the use of AI technologies in healthcare (p\u0026thinsp;=\u0026thinsp;0.029)\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 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAttitude towards AI among medical students across Iraq (n\u0026thinsp;=\u0026thinsp;862).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\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\u003eStrongly 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\u003eNeutral\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\u003eStrongly disagree\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003cp\u003e(male vs. female)*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003cp\u003e(Technology-competent\u003c/p\u003e\u003cp\u003evs. Technology-incompetent)*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI will revolutionize diagnostic specialties.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e182 (21.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e355 (41.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e214 (24.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e86 (10.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI will revolutionize medicine in general.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e180 (20.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e398 (46.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e183 (21.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71 (8.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe human radiologist/pathologist will be replaced in near future.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81 (9.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e214 (24.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e217 (25.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e252 (29.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e98 (11.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe human (non-interventional) physician will be replaced in the near future.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63 (7.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e178 (20.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e199 (23.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e290 (33.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e132 (15.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThese developments frighten me.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98 (11.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e231 (26.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e262 (30.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e165 (19.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e106 (12.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.558\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThese developments make medicine in general more exciting to me.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e182 (21.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e340 (39.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e256 (29.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65 (7.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI will never make the human physician replaceable.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e290 (33.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e303 (35.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e171 (19.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74 (8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24 (2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI will improve diagnostic specialties.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e263 (30.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e400 (46.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e147 (17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40 (4.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI will improve medicine in general.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e278 (32.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e418 (48.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e129 (15.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30 (3.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI should be part of medical training.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e259 (30.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e336 (39.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e202 (23.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e*Mann-Whitney U test was used.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\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 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eReadiness towards AI among medical students across Iraq in terms of ability, vision, and ethics (n\u0026thinsp;=\u0026thinsp;862).\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\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\u003eStrongly 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\u003eNeutral\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\u003eStrongly disagree\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003cp\u003e(Male vs. Female)*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003cp\u003e(Technology-competent\u003c/p\u003e\u003cp\u003evs. Technology-incompetent)*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAbility\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI can utilize AI-based information combined with my professional knowledge.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e206 (23.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e472 (54.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e163 (18.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI can use AI technologies effectively and efficiently in healthcare delivery.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113 (13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e408 (47.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e264 (30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68 (7.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI can use AI applications in accordance with their purpose.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e141 (16.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e434 (50.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e241 (28.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI can access, evaluate, use, share, and create new knowledge using information and communication technologies.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133 (15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e408 (47.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e258 (29.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI can explain how AI applications offer a solution to an appropriate problem in healthcare.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105 (12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e364 (42.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e293 (34.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89 (10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11 (1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI find AI valuable for education, service, and research purposes.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e262 (30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e383 (44.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e169 (19.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI can explain the applications of AI in healthcare services to the patient.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116 (13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e311 (36.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e300 (34.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e116 (13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI can choose the proper application for AI to problems encountered in healthcare.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112 (13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e338 (39.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e317 (36.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81 (9.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVision\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI can explain the limitations of AI technology.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e134 (15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e351 (40.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e296 (34.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69 (8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e134 (15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI can explain the strengths and weaknesses of AI technology.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104 (12.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e393 (45.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e272 (31.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e85 (9.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e104 (12.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI can predict the opportunities and threats that AI technology can create.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e109 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e327 (37.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e315 (36.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e101 (11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e109 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEthics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI can use health data in accordance with legal and ethical norms.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e178 (20.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e396 (45.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e231 (26.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e178 (20.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI can act in accordance with ethical principles while using AI technologies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e149 (17.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e420 (48.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e236 (27.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e149 (17.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.280\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI can follow the legal regulations regarding the use of AI technologies in healthcare.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e170 (19.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e411 (47.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e221 (25.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45 (5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e170 (19.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e*Mann-Whitney U test was used.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, Iraqi medical students expressed an overall positive attitude toward AI. Generally, male students and technology-competent individuals exhibited a more positive attitude across several aspects. The majority of our respondents (62\u0026ndash;80%) believed that AI will revolutionize or improve medical practice, especially for specialties that relies on imagining such as radiology and pathology. With only 3.6% of the students reported no prior use of AI, the current generations of students are widely exposed to technological advancement which make it easier for them to adopt and accept the AI innovations in all aspects of life, including medicine.\u003c/p\u003e\u003cp\u003eComparable results were reported across several countries in the Middle East and North Africa (MENA) region. In a large multinational study that involved nine countries from the MENA region, it was reported that nearly 85% of the medical students thought that AI with revolutionize medicine and radiology in particular, with tech-savvy individual significantly held a more positive attitudes towards AI \u003csup\u003e(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/sup\u003e. Likewise, studies from Syria, Saudi Arabia, and Jordan found similar results \u003csup\u003e(\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/sup\u003e. For instance, a study from Syria by Swed et al. (2022) reported a favorable attitude towards AI which was significantly more prevalent among male medical students and doctors. The majority of participants agreed that the application of AI is necessary in the medical field, and would aid in early diagnosis and assessment of disease severity\u003csup\u003e(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA study from Jordan by Rjoop et al. (2025) included medical students and pathology trainees showed an overall favorable attitude towards AI. The study found that 81.4% of participants agreed that AI will revolutionize medicine in general and pathology in particular (79.2%)\u003csup\u003e(20)\u003c/sup\u003e. Two studies from Saudi Arabia further support this trend. Farooq et al. (2024) investigated attitudes toward AI in ophthalmology. The vast majority of the respondents believed that AI play a role in screening (96%), diagnosis (95.9%), and prevention (93%) of ophthalmic diseases\u003csup\u003e(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/sup\u003e. Furthermore, Alabbad et al. (2025) reported that 81.8% of residents, interns, and medical students expressed their belief that AI aids in early diagnosis and disease assessment, and 79.7% considered it essential in medicine\u003csup\u003e(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThese trends of positive attitudes towards AI were not limited to the MENA region. Several studies from industrialized countries like the United Kingdom (UK)\u003csup\u003e(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/sup\u003e, Germany\u003csup\u003e(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/sup\u003e, and Malaysia\u003csup\u003e(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/sup\u003e as well as from non-industrialized countries like Sudan\u003csup\u003e(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/sup\u003e and Pakistan\u003csup\u003e(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/sup\u003e have shown comparable findings. This reflects a widespread of optimism regarding the role of AI in healthcare.\u003c/p\u003e\u003cp\u003eOur study has also identified gender-related differences in attitudes toward AI. Male students were more positive toward AI use and were also significantly more likely to perceive themselves as skilled in technology (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These disparities can be partly attributed to societal norms and cultural expectations, as in Iraq, like several other countries, males often have a greater affinity for technological engagement\u003csup\u003e(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/sup\u003e. Furthermore, students who self-identified as technologically competent demonstrated significantly more favorable attitudes toward AI. This refers to the role of digital literacy as a contributing factor in shaping the attitude toward AI.\u003c/p\u003e\u003cp\u003eThe current study revealed that 40-48.9% of the study population expressed their disagreement with the possibility of replacement of human radiologists, pathologists, or non-interventional physicians in the near future. More than two-thirds of the students believed that \u003cem\u003e\u0026ldquo;AI will never make the human physician replaceable\u0026rdquo;\u003c/em\u003e. However, 38.2% exhibited some fear regarding the recent AI advancement e.g. \u003cem\u003e\u0026ldquo;These developments frighten me\u0026rdquo;\u003c/em\u003e. Moreover, a significant proportion of them indicated that these developments might affect their choice of medical specialty. This fear could be partly related to the anxiety about job security and the possibility of losing some jobs in the future. Additionally, it can be attributed to the ethical and confidential concerns related to the use of AI in healthcare .Medical student throughout several studies have expressed generally cautious reviews regarding the possibility of being replaced by AI in the future\u003csup\u003e(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA study by Al Hadithy et al. (2023) included clinical-year medical students in Oman demonstrated some concerns about the implications of AI on future careers. More than 70% of subjects thought that AI would replace healthcare professionals within 11\u0026ndash;25 years, particularly regarding diagnostic imaging. They also believed that physician job opportunities will be reduced (61.1%) with some specialties are vulnerable to these changes (77.8%)\u003csup\u003e(29)\u003c/sup\u003e. Similarly, nearly half of medical students in Pakistan agreed that AI would replace human medical professionals \u003csup\u003e(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOn the other hand, several studies showed an optimistic view of AI as a complementary tool that supports physicians rather than replacing them. A study in Jordan (2025) revealed that although more than half of the participants believed that AI could be used for automated diagnosis, a large majority of them disagreed that doctors in general (73.9%) or pathologists (63%) would be replaced in the foreseen future\u003csup\u003e(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/sup\u003e. In Palestine (2024), nearly three-quarters of students disagreed that doctors would be completely or partially replaced by AI\u003csup\u003e(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/sup\u003e. Clinical-year students showed higher odds of disagreements on radiologists being replaced by AI compared to pre-clinical students. This could reflect that the clinical exposure to real-world medical complexities could change the students\u0026rsquo; perspective about AI in healthcare and can alleviate some misconceptions about AI\u003csup\u003e(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThese reviews convey that medical students across different countries are aware of the potential transformations of AI to the healthcare; however, they remain skeptical regarding the ability of AI to fully replace physicians in the near future. Their concerns regarding job security, particularly in diagnostic specialties, need attention and further discussion.\u003c/p\u003e\u003cp\u003eOur study revealed that 69% of students expressed interest in learning about AI as part of their medical training. Likewise, two previous studies in Iraq the majority of medical students (\u0026gt;\u0026thinsp;70%) supported the idea of incorporating AI education in their curriculum, and it would be advantageous for their future careers \u003csup\u003e(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/sup\u003e. Similar patterns have been observed in studies conducted in neighboring countries. Medical students and residents have demonstrated an increasing interest in integrating AI into medical education, residency, and fellowship training in Oman (79.6%), Saudi Arabia (75.9%), and Jordan (46.2%) \u003csup\u003e(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/sup\u003e. These results indicate a pressing need to revise and update undergraduate and postgraduate medical curricula so that training aligns with the rapid advancement of AI tools.\u003c/p\u003e\u003cp\u003eTo the best of our knowledge, this is the first study in Iraq that assesses the readiness of university students to AI using a validated tool. A study by Murad \u003csup\u003e(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/sup\u003e found that only 14.5% of students felt confident in their ability to use basic AI tools after graduation. This relied on a single self-perception question rather than a validated instrument. In contrast, our study found that a substantial proportion of respondents, ranging from approximately 50% to 80% expressed confidence in their abilities to adopt AI in healthcare. Technologically-competent individuals consistently perceived themselves as having a higher level of self-reported ability (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eIn the vision component of AI readiness, more than half of the medical students surveyed felt confident in their ability to understand AI's limitations, strengths, and weaknesses, as well as predicting the opportunities and threats AI can create in healthcare. This clearer vision was more evident among technology-competent respondents.\u003c/p\u003e\u003cp\u003eDespite the data security and privacy concerns reported by the majority (84%) of Iraqi medical students in a study by Mahmood et al.\u003csup\u003e(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/sup\u003e, the present study revealed that nearly two-thirds of the medical students demonstrated readiness to adopt ethical and legal principles of AI use in clinical practice; however, this did not significantly differ between technology-competent and non-competent students (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e We reviewed several studies that used the MAIRS-MS questionnaire to assess medical students\u0026rsquo; readiness to integrate AI in healthcare practices. We found broadly similar patterns across different countries. More than half of Jordanian medical students in a study by Hamad et al. exhibited readiness throughout the three domains of ability, vision, and ethics. Notably, students who received formal AI education or a prior course in AI were associated with better readiness scores\u003csup\u003e(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/sup\u003e. A study by Baseer, S. et al. (2025) in Pakistan found that approximately two-thirds of medical and dental students showed readiness to AI in the three domains of ability, vision, and ethics, with more male students being engaged \u003csup\u003e(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn Malaysia, Tung, A. et al (2023) found that while students reported comparable levels of readiness in the vision and ethics domains (\u0026gt;\u0026thinsp;50%), their confidence in the ability domain was notably lower (\u0026lt;\u0026thinsp;50% for most items) compared to our findings. This could partly reflect the rapid advancements and increasing familiarity with AI that have occurred over the past two years\u003csup\u003e(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThere was positive and negative attitudes towards AI, but positive attitudes outweigh negative attitudes. Medical students expressed high confidence in their readiness to adopt AI in healthcare. It is essential to understand that use of AI in medicine carries its own risks, for instance, ethical aspects and confidentiality of the patients, concerns about safety and efficacy of decisions,\u003c/p\u003e\u003cp\u003eOur vision aligns with expert recommendations from other countries \u003csup\u003e(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/sup\u003e. We believe that incorporation of AI in medical curriculum needs to highlight understanding the basics of AI, how to deal with medical datasets, AI-driven medical applications and scenarios, emphasis on AI's ethical and legal domains, and limitations and drawbacks of AI. Moreover, we recommend that AI training is provided to younger students to adopt AI innovations and gain confidence in interacting with technology. Incorporating AI in the medical curriculum for undergraduate medical student helps to address their fears and concerns. Ensuring that they realize that AI is likely to support not replace doctors and the use of AI still requires professional supervision and monitoring, as it cannot ethically be considered responsible \u003csup\u003e(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe acknowledge several limitations in our study. First, we relied on self-reported data, which could be subjected to social desirability and selective memory of participants. Second, a cross-sectional study design cannot establish causality. Third, the study did not capture all medical colleges across Iraq. We recommend that future longitudinal study designs, including more colleges particularity in north and west Iraq to verify the generalizability of the results.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTo the best of our knowledge, this is the first study in Iraq to assess medical students\u0026rsquo; attitudes and readiness toward AI using validated tools. We found that a predominantly favorable attitudes towards AI with interest in integrating AI into the medical curriculum. Medical students demonstrated confidence in their ability to work with AI, a clear vision of its limitations and weaknesses, and their willingness to follow the ethical principles and legal regulations. These findings highlight the need to incorporate structured AI training into medical curricula to enhance students\u0026rsquo; ability, vision, and ethical preparedness for the future of AI-driven medicine.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArtificial Intelligence\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eECG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eElectrocardiogram\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIBM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInternational Business Machines Corporation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAIRS-MS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMedical Artificial Intelligence Readiness Scale for Medical Students\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMENA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMiddle East and North Africa\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSPSS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUK\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUnited Kingdom\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUSA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUnited States of America\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Omar A. Al-Saffar for his assistance in data collection for this study and all the medical students who participated in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.A.A. served as the principal investigator, leading study conception, methodology, supervision of data collection, performing data analysis, preparing tables, and drafting the manuscript.\u003c/p\u003e\n\u003cp\u003eR.A.A. and M.S.I. contributed to drafting the introduction and critically reviewing manuscript. M.S.I., I.S., M.A.T.A., H.A., A.Y.K., and H.A.H. contributed to data collection, survey distribution, and literature synthesis.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures in this study were conducted in accordance with the ethical standards of the institutional and national research committees and the Declaration of Helsinki (1964), as revised in 2013. Ethical approval was granted by the Ministry of Higher Education, University of Basrah, College of Medicine. The participants were fully informed about the research and provided their consent. The collected data were used exclusively for the purpose of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenerative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChatGPT (OpenAI, GPT-5) was used in a limited manner under the supervision of the authors to improve phrasing, grammatical accuracy, and formatting consistency. The AI tool was not involved in study design, data collection, analysis, interpretation, or drawing scientific conclusions. The authors take full responsibility for the integrity and accuracy of the content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStandards of Reporting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study involving human participants adhered to the STROBE guidelines.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSociety for the Study of Artificial Intelligence and Simulation of Behavior. What is artificial intelligence? [Internet]. 2014 [accessed 2025 Oct 8]. 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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. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/23821205241281648\u003c/span\u003e\u003cspan address=\"10.1177/23821205241281648\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaseer S, Jamil B, Khan SA, Khan M, Syed A, Ali L. Readiness towards artificial intelligence among medical and dental undergraduate students in Peshawar, Pakistan: a cross-sectional survey. BMC Med Educ. 2025;25(1):632.\u003c/span\u003e \u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12909-025-06911-7\u003c/span\u003e\u003cspan address=\"10.1186/s12909-025-06911-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Attitude, Readiness, Iraq, Medical Education, Medical Students","lastPublishedDoi":"10.21203/rs.3.rs-8169603/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8169603/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) has emerged as a transformative force in healthcare, enhancing diagnostic precision, predicting outcomes, and optimizing administrative processes. In medical education, AI supports learning through simulation, automated assessments, and diagnostic training. However, successful implementation relies on clinicians\u0026rsquo; and students\u0026rsquo; readiness to adopt AI technologies.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eThis study aims to assess the attitudes of Iraqi undergraduate medical students toward AI in healthcare and to evaluate their readiness across three key domains: ability, vision, and ethics for the integration of AI into medical education and clinical practice.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional study was conducted among 4th\u0026ndash;6th-year medical students across Iraq from July 22 to September 1, 2025. Data were collected using a validated online questionnaire distributed via social media. The questionnaire consisted of three sections: (1) sociodemographic characteristics and AI exposure; (2) attitudes toward AI, using a modified version of the questionnaire developed by Pinto dos Santos et al.; and (3) readiness for AI integration, evaluated using a 14-item version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), covering the domains of ability, vision, and ethics. Cronbach\u0026rsquo;s alpha values of 0.856, 0.793, 0.825, and 0.880 for the respective domains and overall scale. Data were analyzed using SPSS v26. Mann\u0026ndash;Whitney U test and Chi-square test were used to compare groups, with statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 862 students responded (mean age 22.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62 years; 50.2% male). Nearly two-thirds used AI in both academic and personal contexts, and 55.7% considered themselves technologically skilled. Overall, \u0026gt;\u0026thinsp;60% agreed that AI will revolutionize diagnostic specialties and medicine and should be included in medical curricula. Readiness analysis revealed high agreement in the ability domain (\u0026gt;\u0026thinsp;70%) and moderate agreement for vision (\u0026gt;\u0026thinsp;5%), with strong ethical awareness (\u0026gt;\u0026thinsp;65%). Male and technologically skilled students demonstrated significantly more positive attitudes and overall readiness (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIraqi medical students exhibited generally positive attitudes and moderate readiness toward AI integration in healthcare. Technological competence significantly influenced both attitude and readiness levels. The study emphasizes the need for tailored educational programs to enhance preparedness for the future of AI-driven medicine.\u003c/p\u003e","manuscriptTitle":"Attitudes and Readiness of Medical Students in Iraq towards Artificial Intelligence: A Cross-Sectional Study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 12:39:40","doi":"10.21203/rs.3.rs-8169603/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-17T04:43:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T18:39:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-30T21:17:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234268844118696105696444603027100250951","date":"2026-01-22T17:29:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32390468234459785649761715906988306963","date":"2026-01-21T12:04:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339372418470448159702157301844232170120","date":"2026-01-20T17:16:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-25T12:37:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48716426657064382741462523437391035711","date":"2025-12-05T09:26:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-05T08:16:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-25T03:42:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-25T03:42:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-11-21T04:49:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"296092b1-5725-479e-85c5-8c38ac524cca","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T05:55:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 12:39:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8169603","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8169603","identity":"rs-8169603","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}