Assessment of Medical Students’ Perception and Knowledge Toward Artificial Intelligence and its Medical Applications among a Sample of New Giza University Students: A Cross-sectional Study

Assessment of Medical Students’ Perception and Knowledge Toward Artificial Intelligence and its Medical Applications among a Sample of New Giza University Students: A Cross-sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessment of Medical Students’ Perception and Knowledge Toward Artificial Intelligence and its Medical Applications among a Sample of New Giza University Students: A Cross-sectional Study Mohamed Adel Elshobasy, Youssef Wafik Aziz, Mohamed Saad Abdelnaby, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9075059/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The rapid integration of Artificial Intelligence (AI) into clinical practice necessitates preparing future physicians for such interaction. This study assessed medical students’ knowledge, attitudes, and perceptions towards clinical AI and its integration into medical curricula and identified their preferred topics and modes of delivery for AI education. Methods A cross-sectional survey using a validated questionnaire was conducted with 334 undergraduate medical students at New Giza University, Egypt. Participants were questioned about their knowledge, attitudes, and perceptions (KAPs) toward AI in medicine, and their preferred AI topics and modes of education delivery. Chi-square testing analyzed associations between students’ responses and their demographics. Results Analysis of the responses revealed that 71.9% of students do not understand fundamental AI concepts, and 60.5% cannot cite recent clinical AI advancements. Furthermore, 69.1% express concern about AI’s ethical implications in medicine. Despite this, 89.8% recognize the importance of AI in the future of medicine and 91% desire further AI education. Preferred topics included when to use AI, strengths and weaknesses of AI, and ethics of AI. The preferred modes of education were short lectures, workshops, and symposia. No significant differences were found between students’ KAPs and their academic year. Conclusion A substantial gap exists in medical students’ knowledge and perception of AI in medicine; yet they strongly recognize its significance and are eager to learn about it for three hours or less per month. To address this, curricular developers should prioritize clinically oriented topics – AI’s clinical applications, ethical implications, and the strengths and limitations - delivered in concise, interactive formats. Key learning outcomes must include the ability to critically appraise AI technologies, evaluate their outputs, and recognize limitations. Medical educators should embed these topics into existing teaching without overburdening students, cultivating future physicians confident in using AI and navigating its ethical challenges. Artificial intelligence AI Medical education Knowledge Perception Attitude Undergraduate Medical students Egypt New Giza Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Artificial intelligence (AI) is a computer system that enables machines to accomplish jobs that would ordinarily need human intelligence such as speech recognition, decision-making, and pattern recognition ( 1 ). AI technologies include primarily machine learning, deep learning, natural language processing, and computer vision ( 2 ). These different AI technologies have been steadily incorporated into medicine since the mid-1960s ( 3 ). However, recent years have witnessed a rapid integration that is reshaping multiple medical fields. For instance, in ophthalmology, AI systems like IDx-DR and EyeArt have been FDA-approved for screening diabetic retinopathy, achieving sensitivity and specificity rates exceeding 90% in real-world primary care settings ( 4 ). In orthopedics, commercial fracture diagnosis tools like BoneView™ have been shown to reduce diagnostic errors by up to 3 times in prospective studies ( 5 ). Finally, in oncology, deep learning models such as 'LUMAS' have outperformed radiologists in predicting 1-year lung cancer risk from CT scans, achieving an AUC of 0.94 ( 6 ). These rapid advancements of AI applications in medicine not only enhanced the quality of healthcare services but also led to cost savings for healthcare systems( 7 ). As such, in 2018, the American Medical Association adopted a policy called “Augmented Intelligence in Health Care”, to establish a foundation that helps AI grow and advance in healthcare. This policy necessitates the adequate preparation of medical students to get acquainted with and accept AI technologies as supporting tools in healthcare ( 8 ) . While the rationale behind including AI topics in medical education has been established, there is still a need to assess the level of knowledge and perception of medical students and their attitude towards integrating AI into their curricula to identify knowledge gaps and misconceptions that need to be addressed through targeted education. Studies from various countries have investigated the knowledge, attitude, and perception of medical students towards AI in the healthcare field ( 9 – 12 ). However, existing literature predominantly focuses on knowledge assessment rather than providing actionable data for curriculum design. Effective curriculum development, as articulated in Kern’s six-step framework, begins with a comprehensive needs assessment that identifies both perceived needs of learners and the actual gaps between current and desired competencies ( 13 ). Therefore, this study aims to investigate the KAP of medical students towards AI and identify the specific content and methods that resonate with medical students. The analysis of the survey results will help identify knowledge gaps and misconceptions about AI, as well as establish an evidence base to inform the strategic integration of AI content within the medical curriculum. Methodology Study design This work introduces the results of a cross-sectional study that took place within the premises of New Giza University (NGU) (a multidisciplinary private university located in Giza governorate, Egypt). The data collection was conducted over a duration of approximately 5 months during the academic year 2024–2025. Study tools and data collection The study was based on a two-part questionnaire (Additional Files 1). The first part covers background and demographic information. The second part of the questionnaire was adapted (with permission) from a questionnaire conducted by Liu et al. ( 14 ). This part of the questionnaire assesses the students' current perception and knowledge of artificial intelligence (AI) in medicine. It also investigates the students’ interest in learning more about AI, topics of students’ interest in AI, and what resources students believe would be the most useful for their medical school to offer. A pilot study was first conducted on a small group of 30 students to check the validity, consistency, and clarity of the survey questions. Those students gave valuable feedback to improve readability, flow, and understanding of the survey. Pilot participants were excluded from the final study sample. Study population The recruitment process targeted undergraduate medical students at NGU. The inclusion criteria were to be enrolled as an NGU medical student in the academic year 2024–2025 and at least finishing 1 academic year at the School of Medicine at NGU. Any survey respondents not satisfying these criteria were excluded. Sample size and sampling technique. The appropriate sample size for the study was calculated to be 322, using EPI Info™ StatCalc version 7. This number was set based on an expected total population of around 2000 students at NGU School of Medicine, an expected frequency of 50% given the highest sample size, and a 5% margin of error. Convenience sampling was employed to collect the survey responses. The study collected a total of 355 respondents, those with contradictory responses were manually detected and excluded. This left a total of 334 complete valid responses. Statistical analysis. Data analysis and visualization was done using IBM® SPSS® Statistics 27.0 and Microsoft® Excel® (version 2409), respectively. The 5-point Likert scale values were grouped into a 3-point Likert scale (agree, neither agree nor disagree, disagree) to facilitate clearer interpretation and reduce response variability. Chi-square tests were used to assess the significance of differences in students’ KAPs based on their demographic variables. A p-value less than 0.05 was considered statistically significant. Results Medical students’ demographics Table 1 (To be inserted above this paragraph) shows the sociodemographics of the participating medical students. The study involved 334 medical students at New Giza University with a mean age of 20.78 ± 1.954 years. Gender distribution revealed that 55.4% of participants were females and 44.6% were males. The survey encompassed students from all stages of medical school- except for the first year. More than half of students’ parents held a post-graduate degree, as opposed to a smaller proportion with a university degree, secondary education, or less. Table 1 General and Demographic Characteristics of the Study Participants (n = 334) Variable Category Count Gender Male 149 (44.6%) Female 185 (55.4%) Age (Years) 20 or less 160 (47.9%) More than 20 174 (52.1%) Mean ± SD 20.78 ± 1.954 Nationality Egyptian 331 (99.1%) Non-Egyptian 3 (0.9%) Academic Year Year 2 100 (29.9) Year 3 52 (15.6) Year 4 56 (16.8) Year 5 (Final) 126 (37.7) Clinical practice experience / work shadowing / observership Yes 173 (51.8%) No 161 (48.2%) Father’s educational degree Secondary or less 17 (5.1%) University 114 (34.1%) Postgraduate 203 (60.8%) Mother’s educational degree Secondary or less 17 (5.1%) University 150 (44.9%) Postgraduate 167 (50.0%) Academic background in tech field Yes 144 (43.1%) No 190 (56.9%) Additional AI training in computer science Yes 46 (13.8%) No 288 (86.2%) When addressing students’ past experiences, 51.8% of participants had clinical practice experience. Meanwhile, 56.9% of responders had academic backgrounds in technologically oriented fields such as computer science, IT, robotics, or databases. However, 86.2% of participants had not received AI-focused training in computer science (Table 1 ). Medical students’ knowledge about AI Table 2 Sources of AI Exposure Among Medical Students Where did you gain your exposure to AI? N (%) Media (YouTube, Twitter) 264 (79.0) Television 74 (22.2) Online forums 106 (31.7) Peer-reviewed articles 36 (10.8) Formal lectures 30 (9.0) Professors/doctors 56 (16.8) Books 35 (10.5) Research 50 (15.0) Projects 50 (15.0) Conferences 39 (11.7) Family and friends 136 (40.7) Other 1 (0.3) The survey responses revealed that 71.9% of students felt that they were unable to understand the fundamental concepts of AI (Fig. 1 ). Additionally, 44.1% of respondents stated that their medical school didn’t provide resources for topics about AI in medicine. Notably, 60.5% of participants reported that they were unable to cite recent clinically relevant AI-research, compared to 21.6% of participants who believed they could do so. Despite this, 71.3% of respondents stated they could list some benefits for the integration of AI in the medical field. Nearly 53.6% of respondents believed that it's hard to understand AI due to social media sensationalism. Also, 48.2% of respondents felt capable of distinguishing between ‘hyped’ AI articles and those with true clinical significance, compared to 24.9% who felt they could not. Additionally, the study revealed that the majority of students acquired their knowledge about AI from media (79%), followed by family and friends (40.7%) and online forums (31.7%) (Table 2 ). Only a small proportion gained their understanding through formal lectures (9%) or from medical professionals (16.8%). Medical students’ perception and attitude towards AI In Fig. 2 , 89.8% of participants agreed that AI will play a significant role during their lifetime and 71.2% were excited about incorporating AI into their future medical practices. However, 69.1% of the participants were concerned about the ethical implications of AI in medicine. Furthermore, 91% of respondents were interested in learning about AI in medicine and 87.7% acknowledged the incorporation of AI concepts into their medical education. In addition, 37.5% of students believed that integrating AI would not detract them from their core medical curriculum, while 28.8% expressed concerns about such detraction. Preferred modes and topics to explore AI Upon asking the participants about the most useful ways their university could offer to explore AI topics (Fig. 3 ), they preferred short lectures (63.5%), followed by workshops (60.4%), and then symposia conducted by experts (36.5%). Moreover, 62.9% chose ‘When to use AI’ as the most wanted topic, followed by ‘Strengths and weaknesses of AI in medicine’ (53.6%), and then ‘Ethics of AI’ (49.7%) (Fig. 4 ). In terms of time commitment, 57.3% of participants wanted to dedicate an average of 3 hours or less per month to learn about AI, while the remaining 42.5% wanted to spend more than 3 hours per month (Additional files 2, Table S1 ). Perceived effects of AI on medical specialties When considering medical specialty selection (Table 3 , to be inserted above this paragraph). The top-picked specialties were radiology (55.4%), followed by pathology (33.5%), interventional radiology (23.1%), and general surgery (23.1%). Furthermore, 33.6% of respondents were less likely to choose these selected specialties because of the anticipated integration of AI, while 31.2% were more likely to pursue such specialties- the rest remained neutral. Table 3 Perceived Effects of AI on Medical Specialties According to Study Participants Item Category Number Percentage (%) What specialty do you wish to pursue in the future? Specialized surgery * 58 17.4 Internal medicine ** 107 32.0 General surgery 27 8.1 Obstetrics and gynecology 24 7.2 Pediatrics 18 5.4 Radiology *** 8 2.4 Other **** 40 11.9 I have not decided yet 52 15.6 Which specialties do you think will be most affected by AI Anesthesiology 61 18.26 Dermatology 37 11.08 Emergency medicine 25 7.50 Family medicine 51 15.27 General surgery 77 23.05 Internal medicine 41 12.28 Interventional radiology 77 23.05 Neurosurgery 65 19.46 Neurology 25 7.48 Obstetrics and gynecology 2 0.59 Ophthalmology 33 9.88 Orthopedic surgery 18 5.39 Pathology 112 33.53 Pediatrics 8 2.39 Physical medicine and rehabilitation 20 5.98 Plastic surgery 26 7.78 Psychiatry 13 3.98 Radiology (diagnostic) 185 55.38 Radiation oncology 62 18.56 Urology 3 0.89 Vascular surgery 18 5.38 I am less likely to choose these selected specialties because of the anticipated integration of AI Strongly agree 39 11.7 Somewhat agree 73 21.9 Neutral 118 35.3 Somewhat disagree 62 18.6 Strongly disagree 42 12.6 * Cardiothoracic Surgery, Neurosurgery, Orthopedic surgery, and Plastic surgery. ** Cardiology, Dermatology, Endocrinology, Gastroenterology, Hematology, Immunology, Nephrology, Neurology, Ophthalmology, Otolaryngology, and Respirology. *** Diagnostic Radiology and Interventional Radiology **** Anesthesia, Emergency medicine, Pathology, Public, and Psychiatry. Demographic factors associated with knowledge, attitudes, and perception. The analysis of the study also compared how different independent variables are associated with students’ KAPs. Regarding gender, more females (76.2%) were worried about the ethical considerations of using AI in medicine compared to males (60.4%) (p-value < 0.001). (Additional file 2, Table S2 ). Also, 55.6% of females reported difficulty in understanding AI due to social media sensationalism, compared to 51% of males (p-value = 0.03). No significant differences were observed between genders regarding understanding AI concepts, acceptance of AI integration in their formal medical education or future careers. Notably, students’ academic years had no significant relationship with their knowledge, attitude or perceptions toward AI in medicine (Additional files 2, Table S3). Students with an academic background in technology-oriented fields (like computer science, IT, robotics) demonstrated significantly higher reports of knowledge about AI in medicine when compared to students without such background (Table 4 to be inserted above this paragraph). However, there were no significant differences observed between the two groups regarding their perception of AI in medicine or their attitude towards integrating AI into their medical curricula. Table 4 Association between past technological background and participants’ KAPs towards AI Statement Likert Response Academic background in technology-oriented fields Yes N = 144 (100%) No N = 190 (100%) P-value AI will take on a significant role in medicine during my lifetime Agree Neither agree nor disagree Disagree 132 (91.6%) 9 (6.2%) 3 (2%) 168 (88.4%) 12 (6.3%) 10 (5.2%) 0.329 I am excited about using AI tech as a future physician Agree Neither agree nor disagree Disagree 110 (76.3%) 19 (13.1%) 15 (10.4%) 128 (67.3%) 42 (22.1%) 20 (10.5%) 0.106 I understand AI concepts like: CNN, Cross Validation, ROC AUC, Hyperparameters, Deep Learning, Hidden layers, etc. Agree Neither agree nor disagree Disagree 26 (18%) 27 (18.7%) 91 (63.1%) 20 (10.5%) 21 (11%) 149 (78.4%) 0.009 I can list some examples of recent clinically relevant AI Agree Neither agree nor disagree Disagree 39 (27%) 30 (20.8%) 75 (52%) 33 (17.3%) 30 (15.7%) 127 (66.8%) 0.021 I can list the strengths/benefits of using Al in Medicine Agree Neither agree nor disagree Disagree 111 (77%) 24 (16.6%) 9 (6.2%) 127 (66.8%) 36 (18.9%) 27 (14.2%) 0.044 I can list the weaknesses/pitfalls of using AI in medicine Agree Neither agree nor disagree Disagree 108 (75%) 24 (16.6%) 12 (8.3%) 130 (68.4%) 36 (18.9%) 24 (12.5%) 0.43 It's hard to understand and approach AI because of media sensationalism Agree Neither agree nor disagree Disagree 75 (52%) 49 (34%) 20 (13.8%) 104 (54.7%) 69 (36.3%) 17 (8.9%) 0.362 I can separate 'Hype' AI articles vs clinically relevant AI articles Agree Neither agree nor disagree Disagree 78 (54.1%) 36 (25%) 30 (20.8%) 83 (43.6%) 54 (28.4%) 53 (27.8%) 0.145 I am worried about the ethics of using AI in medicine Agree Neither agree nor disagree Disagree 104 (72.2%) 26 (18%) 14 (9.7%) 127 (66.8%) 40 (21%) 23 (12.1%) 0.567 Some training on AI concepts and related topics during medical school can be useful for my future career Agree Neither agree nor disagree Disagree 127 (88.1%) 15 (10.4%) 2 (1.396%) 166 (87.3%) 15 (8.4%) 8 (4.2%) 0.281 My school offers resources if I want to explore the topic of AI in medicine Agree Neither agree nor disagree Disagree 33 (22.9%) 45 (31.2%) 66 (45.8%) 29 (15.2%) 80 (42.1%) 81 (42.6%) 0.069 I want to learn what medical students should know about AI in medicine Agree Neither agree nor disagree Disagree 134 (93%) 8 (5.5%) 2 (1.396%) 170 (89.4%) 12 (6.3%) 8 (4.2%) 0.305 Learning the relevant topics of AI in medicine will significantly detract me from my medical school curriculum Agree Neither agree nor disagree Disagree 37 (25.6%) 47 (32.6%) 60 (41.6%) 59 (31%) 66 (34.7%) 65 (34.2%) 0.343 Similarly, students who received additional training in computer science, particularly in AI-related topics, exhibited higher knowledge regarding AI in medicine (Additional files 2, Table S4). Finally, no significant differences were noted between parents’ education and their children’s grasp of AI concepts, views on AI in medicine, attitude towards AI integration in their curricula or acceptance of AI in their future career (Additional files 2, Table S5-6). Discussion Lack of AI Knowledge Among Medical Students The aim of this study was to assess the knowledge and perception of undergraduate medical students about AI and its integration into medical practice, as well as their preferences for incorporating AI’s education into their curriculum. The study included 334 medical students from their second year till their final year at NGU. Most students in our study reported a lack of understanding of fundamental AI concepts (71.9%) and were unable to list recent AI advancements in the medical field (60.5%). This is in concordance with a recent study conducted on medical students in Jordan, where 58.9% of students lacked knowledge about AI applications in medicine ( 15 ). Similarly a study conducted among medical students in India found that 52% of the students have heard about AI but possess no knowledge about its applications in medicine ( 12 ). This relative lack of AI knowledge among students could be due to the insufficiency of AI-focused resources in medical curricula. In fact, the majority of students in our study reported that they were not provided with sufficient resources to explore topics of interest in AI. A similar concern was presented by a study conducted in Saudi Arabia, where 59.4% of medical students reported the lack of formal AI education in their university ( 16 ). This trend of inadequate and outdated resources might be due to the rapid developments of AI, which have outpaced the development of educational resources for medical curricula. Another study conducted in Egypt across 1346 medical students and house officers reported that the majority of students (78.3%) think they are knowledgeable about AI ( 17 )- a sharp contrast to our findings. This discrepancy could be explained by the questioning approach used in their study, where they posed a broad query without providing concrete examples or defining the scope of AI knowledge. Such general questions may encourage overestimation of actual knowledge. This interpretation is supported by the participants’ report of poor perception about AI and its applications in healthcare as well as their negative attitude towards AI ( 17 ). This discrepancy between AI perception and knowledge indicates their perceived knowledge did not translate into practical understanding of AI. Recognition of AI’s Importance Our study also found that most students recognized the importance of AI for the future of medicine (89.8%) and were interested in learning more about it (91%). This also aligns with a recent study conducted in an Egyptian public university, where over 80% of medical students believed that AI would revolutionize medical education, and over 85% showed interest in learning about AI applications in medicine ( 18 ). Also, Al-Qerem et al. found that 69.5% of medical students in Jordan believed AI is a highly required tool in medicine, and 64.9% think AI should be part of their training system as medical students ( 15 ). These results reflect the growing recognition of AI’s importance among medical students, indicating the need for curricula to meet their demand for AI literacy. However, in another study in Egypt, the authors reported that 76.4% of participants’ have inadequate perception of AI’s importance and usage in the medical field, with 87.4% expressing negative attitudes towards AI in healthcare ( 17 ). This distinction may be partly attributed to the participants’ reliance on informal resources to gain their knowledge on AI, as 66.9% of participants reported that they acquired AI knowledge through self-study. Such an informal learning approach may provide inaccurate information, leading to misconception and skepticism about AI. Impact of AI on Medical Specialty Selection When it comes to medical specialty selection, only 2.4% of students opted for radiology-related specialties. Furthermore, among those who chose radiology-related fields as one of the ‘top 3 specialties to be impacted by AI,’ 37.5% agreed to the statement that they are less likely to choose any of these fields due to the expected AI integration, compared to 26.2% who disagreed with this statement. This relative avoidance towards radiology-related fields may stem from the common belief that AI integration in radiology could reduce demand for human expertise in these fields. In fact, Jackson et al. ( 14 ) reported that most students expressed that their anxiety over potential “displacement” by AI technologies in radiology might deter them from pursuing this specialty. Such concerns could be plausible due to the rapid advancement of AI in radiology, which may even outperform human capabilities in certain cases ( 19 ). However, several recent studies have been increasingly supporting the view of AI as an adjunct to radiologists rather than a replacement. For instance, Purkayastha et al. ( 20 ) demonstrated that AI-assistance radiologists achieved a significant reduction in diagnostic time and exhibited better diagnostic accuracy. This suggests that if physicians accept AI integration in their field, they may use AI to enhance their clinical impact, rather than view it as a competing force. Demand for Formal AI Education The analysis of our study also found that the students’ main sources of knowledge about AI were media (79%), family and friends (40.7%), and online forums (31.7%), while only a minority gained their knowledge from formal lectures (9%), or from doctors and professors (16.8%). These results indicate that students rely mostly on informal resources rather than established academic studies. Such reliance could potentially lead to false perceptions or incomplete understanding of AI’s role in medical practice, especially in the light of the students’ highly reported concerns regarding AI ethics in medicine. A study conducted on 1047 radiologists found that there is an inverse relationship between the level of fear among radiologists and their level of knowledge on AI, suggesting that proper training on AI-related topics could alleviate concerns and promote its use in clinical practice ( 21 ). Moreover, although students currently rely on external, non-curricular resources for AI education, the majority of the respondents in our study (87.7%) think that introducing academic training on AI-related topics to be useful, while only few saw such a step as a detraction from the core medical curriculum. Similarly, a study conducted among medical and health science students across four Arab countries found that 51% of participants held a positive attitude toward integrating AI into health professional education, with Egypt representing the highest percentage within this group ( 22 ). These results suggest that including AI topics in formal medical education will be welcomed and embraced by most students. This is an important point to consider, especially in the light of the significant efforts required to overcome the challenges associated with implementing such integration effectively ( 23 ). Recommendations The study findings highlight key considerations for designing AI curricula in medical education. Given that 57.3% of students preferred to dedicate three hours or less per month to AI education, we recommend that medical educators prioritize the seamless integration of AI topics into existing clinical and pre-clinical teaching to minimize curricular disruption. Students identified three priority topics: clinical indications for AI use (62.9%), strengths and limitations of AI in medicine (53.6%), and ethical implications (49.7%). These preferences reflect a demand for critical appraisal skills rather than technical programming knowledge. We recommend that curricula emphasize clinically oriented topics, enabling students to develop competency in evaluating AI tools' appropriateness, interpreting their outputs, and recognizing their limitations. Given the students' preference for short lectures (63.5%) and workshops (60.4%), it is recommended that curricular frameworks prioritize concise, active learning methodologies over traditional lengthy lecture formats to enhance engagement and critical thinking. Finally, our finding that students may avoid radiology and related specialties due to AI- integration highlights the need to address misconceptions about AI's role. We recommend incorporating exposure to AI models within clinical rotations, where students observe radiologists and other specialists using AI tools to enhance diagnostic accuracy and efficiency. Such exposure may alleviate replacement fears by demonstrating AI's potential to augment rather than supplant physician expertise. Strengths and Limitations A notable strength of the current study is that it stands, to the best of our knowledge, as one of the first to investigate the KAPs -as well as the preferred topics and modes of education- of AI among undergraduate medical students in Egypt. The study also offers a comprehensive comparison of the KAPs of students across different demographics (Additional files 2). Such analysis could highlight specific areas for a more personalized AI integration into medical curricula. Also, our study focused on students across all academic years in medical school except for first-year medical students who were just enrolled by the time the survey was conducted. Indeed, the medical knowledge and university experience at that time were probably insufficient for the first-year students to make sound judgment on the role of AI in medical education. However, our study had some limitations that could be avoided in further studies. Firstly, the use of a cross-sectional design limits the ability to draw conclusions about the causation and the temporal relationships between variables. It also limits the depth of insights into students’ true perceptions and attitudes. Secondly, the use of convenience sampling limits the generalizability of the findings, as the sample may not fully represent the broader population of medical students. This restricts the extent to which results can be applied across different institutions. Future studies employing probability-based sampling methods would provide a more comprehensive understanding of students’ KAPs on AI. Finally, the study’s reliance on self-reported data introduces potential biases like recall bias, social desirability, or response bias. Alternative approaches such as conducting interviews could provide richer qualitative data ( 14 ). Future Perspectives Future research could benefit from incorporating objective performance-based assessments to evaluate students’ knowledge of AI or proficiency in using AI tools. For instance, instruments such as the AI literacy test ( 24 ), AI literacy Concept Inventory (AI-CI) ( 25 ), and SAIL-4-ALL scale ( 26 ) could be used to evaluate foundational AI literacy and skills in a standardized and structured manner. Beyond assessment, the next step for medical education is to conduct studies that evaluate frameworks designed for seamless and efficient AI integration. For example, the low-dose, high-frequency (LDHF) model (27)—which employs brief, interactive, and simulation-based learning activities that prioritize practical application and critical appraisal over technical programming: and the spiral curriculum framework ( 28 ), which distributes content longitudinally to introduce foundational concepts in pre-clinical years and revisits them with increasing complexity during clinical rotations. Investigating the effectiveness and longitudinal impact of these approaches will help identify best practices for implementation and ensure future physicians are equipped to navigate the clinical and ethical challenges of evolving healthcare settings Conclusion The results of the study revealed that most medical students lack knowledge and perception of AI in the medical field. However, they recognized the importance of AI for the future of medicine and showed interest in learning more about it. Students prefer concise, practical learning formats focused on clinical applications, ethical considerations, and the capabilities and limitations of AI. Integrating AI into medical education should extend beyond basic AI literacy. It should aim to cultivate future physicians who can critically appraise AI tools, understand their clinical applications and limitations, and navigate associated ethical challenges with confidence. The findings of this study can guide curriculum development amongst medical schools, as well as help modernize and improve medical education, ensuring that medical students are better prepared to use emerging technologies effectively in their medical practice. Abbreviations AI Artificial Intelligence NLP Natural Language Processing KAP Knowledge, Attitude, and Perception AMRD Age Related Macular Degeneration SPECT Single Photon Emission Computed Tomography NGU New Giza University SD Standard Deviation IQR Interquartile Range Declarations Ethical Approval and consent to participate. The study protocol was reviewed and approved by the “New Giza Research Ethics Committee” at New Giza University (with approval code N-33-2024) to ensure compliance with ethical standards and participants’ confidentiality. Survey responses were collected anonymously, and no personal identifying information was collected. Study participant agreed on a consent form indicating the purpose of the study, statements on participant’s anonymity and confidentiality, and the estimated time for finishing the survey (5-7 minutes). This Informed consent was obtained from all participants prior to data collection. This study adheres to the ethical principles outlined in the Declaration of Helsinki. Consent for Publication Not Applicable Availability of Data and materials All data generated or analyzed during this study are in this published article and its supplementary material Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding Not Applicable Clinical Trial Number Not Applicable Authors’ Contributions All authors contributed to the study conception and design. Questionnaire preparation was performed by ME, YA, MA, RM, OH, and MT. All authors contributed to data collection. Data analysis was performed by ME and SE. The initial draft was prepared by ME, YA, SE, MA, and RM. Revision of the first draft was done by MR, ME , YA, MA, SE, RM, OH, MT, and DB. The work was supervised by SE, MR, and DB. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgment The authors would like to acknowledge the assistance of Mohamed Ayman, Abdullah Karam, Ayman Elsayed, Mohamed Khater, and Omar Fouda with data collection. Also, the authors would like to acknowledge Dr. David Shalom Liu (College of Medicine and Life Sciences, University of Toledo, Toledo, OH, United States) and Dr. Bina Joe (Department of Physiology and Pharmacology, College of Medicine and Life Sciences, University of Toledo, Toledo, OH, United States) for allowing us to use their questionnaire. Author’s Information ME , MBBCH Medical Intern at Cairo University Hospital, received MBBCH from New Giza University in 2024. Faculty of Medicine, New Giza University, Giza, Egypt. Email: [email protected] ORCID: 0009-0003-4008-7067 Research interests: AI applications in medical education and healthcare, including machine learning applications in infectious diseases and non-interventional cardiology. YA , MBBCH Medical Intern at Cairo University Hospital, received MBBCH from New Giza University in 2024. Faculty of Medicine, New Giza University, Giza, Egypt. Email: [email protected] ORCID: 0009-0002-3474-2259 MA, MBBCH Medical Intern at Cairo University Hospital, received MBBCH from New Giza University in 2024. Faculty of Medicine, New Giza University, Giza, Egypt. Email: [email protected] . ORCID: 0009-0009-2502-1557 MR , Ph. D. A Full Professor with the School of Information Technology, New Giza University, and the Department of Biomedical Engineering and Systems in Cairo University. He received the B. Sc. Degree in biomedical engineering and systems in 2001, the B. Sc. Degree in mathematics from Cairo University in Egypt in 2003, the M. Sc. Degree in biomedical engineering and systems in 2005, and the M. Sc. And Ph. D. degrees in computer and information science and engineering from the University of Florida. Gainesville, FL, USA, in 2012 and 2013, respectively. His research interests include biomedical signal processing, medical imaging, information security and forensics, machine learning, image processing, computer vision, and applied mathematics Email: [email protected] ORCID: 0000-0001-9869-0270 RM , MBBCH Medical Intern at Cairo University Hospital, received MBBCH from New Giza University in 2024. Faculty of Medicine, New Giza University, Giza, Egypt. Email: [email protected] ORCID: 0009-0001-5383-0775 OA , MBBCH Medical Intern at Cairo University Hospital, received MBBCH from New Giza University in 2024. Faculty of Medicine, New Giza University, Giza, Egypt. Email: [email protected] ORCID: 0009-0001-9134-0136 MT , MMBCH Medical Intern at Cairo University Hospital, received MBBCH from New Giza University in 2024. Faculty of Medicine, New Giza University, Giza, Egypt. Email: [email protected] ORCID: 0009-0002-9581-4543 DB, MD Professor and head of Public Health department, School of Medicine, at New Giza University. Received her MD of Public Health and Community Medicine in 2008 from Ain Shams University, Giza, Egypt. Email: [email protected] ORCID : https://orcid.org/0000-0002-9800-1751 SE, MD Lecturer in School of Medicine, Public health department, at New Giza University. Researcher at Community Medicine Research Department, National Research Center. Received his MD in Public Health and Community Medicine in 2021 from Cairo University, Giza, Egypt. Email: [email protected] ORCID: https://orcid.org/0000-0002-9144-244X References Mukhamediev RI, Popova Y, Kuchin Y, Zaitseva E, Kalimoldayev A, Symagulov A, et al. Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges. Mathematics. 2022;10(15):2552. 10.3390/math10152552 . Sengar SS, Hasan AB, Kumar S, Carroll F. Generative artificial intelligence: a systematic review and applications. Multimed Tools Appl. 2024;84(21):23661–700. 10.1007/s11042-024-20016-1 . Hirani R, Noruzi K, Khuram H, Hussaini AS, Aifuwa EI, Ely KE, et al. Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities. Life. 2024;14(5):557. 10.3390/life14050557 . Balas M, Micieli JA, Wong JCY. Integrating AI with tele-ophthalmology in Canada: a review. Can J Ophthalmol. 2025;60(3):e337–43. 10.1016/j.jcjo.2024.08.013 . Elkohail A, Soffar A, Khalifa AM, Omar I, Mosaad M, Abdulaziz M et al. AI-Enhanced Surgical Decision-Making in Orthopedics: From Preoperative Planning to Intraoperative Guidance and Real-Time Adaptation. Cureus. 2025 Sep 19. 10.7759/cureus.92762 Chang TG, Park S, Schäffer AA, Jiang P, Ruppin E. Hallmarks of artificial intelligence contributions to precision oncology. Nat Cancer. 2025;6(3):417–31. 10.1038/s43018-025-00917-2 . PubMed PMID: 40055572; PubMed Central PMCID: PMC11957836. Van Leeuwen KG, Meijer FJA, Schalekamp S, Rutten MJCM, Van Dijk EJ, Van Ginneken B, et al. Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment. Insights Imaging. 2021;12(1):133. 10.1186/s13244-021-01077-4 . Making Policy on Augmented Intelligence in Health Care. AMA J Ethics. 2019;21(2):E188–191. 10.1001/amajethics.2019.188 . Chakri I, El Khayali O, Lahlou L. Knowledge and Perceptions of AI Among Medical Students in Morocco: Cross-Sectional Study. JMIR Form Res. 2025;9:e66156–66156. 10.2196/66156 . Amiri H, Peiravi S, Rezazadeh Shojaee SS, Rouhparvarzamin M, Nateghi MN, Etemadi MH, et al. Medical, dental, and nursing students’ attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis. BMC Med Educ. 2024;24(1):412. 10.1186/s12909-024-05406-1 . Laupichler MC, Aster A, Meyerheim M, Raupach T, Mergen M. Medical students’ AI literacy and attitudes towards AI: a cross-sectional two-center study using pre-validated assessment instruments. BMC Med Educ. 2024;24(1):401. 10.1186/s12909-024-05400-7 . Jackson P, Ponath Sukumaran G, Babu C, Tony MC, Jack DS, Reshma VR, et al. Artificial intelligence in medical education - perception among medical students. BMC Med Educ. 2024;24(1):804. 10.1186/s12909-024-05760-0 . Kern DE, editor. Curriculum development for medical education: a six-step approach. 2 ed. Baltimore, Md: Johns Hopkins Univ.; 2009. p. 253. Liu DS, Sawyer J, Luna A, Aoun J, Wang J, Boachie, Lord, et al. Perceptions of US Medical Students on Artificial Intelligence in Medicine: Mixed Methods Survey Study. JMIR Med Educ. 2022;8(4):e38325. 10.2196/38325 . Al-Qerem W, Eberhardt J, Jarab A, Al Bawab AQ, Hammad A, Alasmari F, et al. Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions’ students in Jordan. BMC Med Inf Decis Mak. 2023;23(1):288. 10.1186/s12911-023-02403-0 . Alabbad FA, Almeneessier AS, Alshalan MH, Aljarba MN. Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Saudi Arabia. J Fam Med Prim Care. 2025;14(4):1459–64. 10.4103/jfmpc.jfmpc_1812_24 . Allam RM, Abdelfatah D, Khalil MIM, Elsaieed MM, El Desouky ED. Medical students and house officers’ perception, attitude and potential barriers towards artificial intelligence in Egypt, cross sectional survey. BMC Med Educ. 2024;24(1):1244. 10.1186/s12909-024-06201-8 . Khater AS, Zaaqoq AA, Wahdan MM, Ashry S. Knowledge and Attitude of Ain Shams University Medical Students towards Artificial Intelligence and its Application in Medical Education and Practice. Educ Res Innov J. 2023;3(10):29–42. 10.21608/erji.2023.306718 . McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89–94. 10.1038/s41586-019-1799-6 . Purkayastha S, Isaac R, Veldandi A, Saxena R, Singh P, Vaswani P et al. Measuring Impact of Radiologist-AI Collaboration: Efficiency, Accuracy, and Clinical Impact. In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI) [Internet]. Athens, Greece: IEEE; 2024 [cited 2024 Nov 6]. pp. 1–4. Available from: https://ieeexplore.ieee.org/document/10635618/doi : 10.1109/ISBI56570.2024.10635618 Huisman M, Ranschaert E, Parker W, Mastrodicasa D, Koci M, Pinto De Santos D, et al. An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude. Eur Radiol. 2021;31(9):7058–66. 10.1007/s00330-021-07781-5 . Issa WB, Shorbagi A, Al-Sharman A, Rababa M, Al-Majeed K, Radwan H, et al. Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey. BMC Med Educ. 2024;24(1):1166. 10.1186/s12909-024-06076-9 . Masoumian Hosseini M, Masoumain Hosseini T, Qayumi K. Integration of Artificial Intelligence in Medical Education: Opportunities, Challenges, and Ethical Considerations. J Med Educ. 2024;22(1). 10.5812/jme-140890 . Hornberger M, Bewersdorff A, Nerdel C. What do university students know about Artificial Intelligence? Development and validation of an AI literacy test. Comput Educ Artif Intell. 2023;5:100165. 10.1016/j.caeai.2023.100165 . Zhang H, Perry A, Lee I. Developing and Validating the Artificial Intelligence Literacy Concept Inventory: an Instrument to Assess Artificial Intelligence Literacy among Middle School Students. Int J Artif Intell Educ. 2024 May;5. 10.1007/s40593-024-00398-x . Soto-Sanfiel MT, Angulo-Brunet A, Lutz C. The Scale of Artificial Intelligence Literacy for all (SAIL4ALL): A Tool for Assessing Knowledge on Artificial Intelligence in All Adult Populations and Settings [Internet]. SocArXiv; 2024 [cited 2024 Dec 3]. Available from: https://osf.io/bvyku 10.31235/osf.io/bvyku Jhpiego. Low dose, high frequency: a learning approach to improve health workforce competence, confidence, and performance [Internet]. Baltimore (MD): Jhpiego; 2013 [cited 2026 Feb 22]. Available from: https://hms.jhpiego.org/wp-content/uploads/2016/08/LDHF_briefer.pdf Harden RM. What is a spiral curriculum? Med Teach. 1999;21(2):141–3. 10.1080/01421599979752 . Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.pdf File name and format: Additional File 1 Format: PDF (Adobe Acrobat) Title of data: Assessment of Undergraduate Medical Students’ Perception and Knowledge Toward Artificial Intelligence and Its Applica on in Medicine among a Sample of New Giza University Students: A Cross-Sectional Study Description of data: The questionnaire used for this study. Additionalfile2.xlsx File name and format: Additional File 2 Format: Excel (.xlsx) Title of data: Supplementary materials for paper titled: “Assessment of Medical Students’ Perception and Knowledge Toward Artificial Intelligence and its Medical Applications among a Sample of New Giza University Students: A Cross-sectional Study.” Description of data: 6 tables including preferred time to study AI-topics in medicine and tables showing the relationship between students’ sociodemographics and their KAPs. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9075059","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607746748,"identity":"e0cfd03f-7fcb-49ff-ab7c-34d586246b1b","order_by":0,"name":"Mohamed Adel Elshobasy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYBACgwM8IIoZiBMYD3yACuLVYomkheHgDGK02CNrOcxDjBaz470HP92osGbgb08+cNh2R21iA3vzNgmGilrcWs6cS5bOOZPOIHHmWcLh3DPHExt4jpVJMJw5jlvLjRwD6dy2wwwMQMbh3LZjiQ0SOWYSjG3HcGoxuJFj/Dv332EG+Rv5Hw5bgrTIvwFq+YdXi5l0bsNhEIPhMGNbDdAWHqCWhhrcWs6cMbPOOZbOY3jmmcHB3rYDxm08acUWCccO4NZyvMf4dk6NtZzc8eSHD3621cn2sx/eeONDTR1OLTAAjRGGwwxsIAoYRwS1wEAdBmMUjIJRMApGAQAJL17+jNt11QAAAABJRU5ErkJggg==","orcid":"","institution":"New Giza University","correspondingAuthor":true,"prefix":"","firstName":"Mohamed","middleName":"Adel","lastName":"Elshobasy","suffix":""},{"id":607746749,"identity":"7efb8c0b-66e9-4325-8c59-f68ecd4fe382","order_by":1,"name":"Youssef Wafik Aziz","email":"","orcid":"","institution":"New Giza University","correspondingAuthor":false,"prefix":"","firstName":"Youssef","middleName":"Wafik","lastName":"Aziz","suffix":""},{"id":607746755,"identity":"c76b7204-5f90-401c-9681-ac0747ce1f0c","order_by":2,"name":"Mohamed Saad Abdelnaby","email":"","orcid":"","institution":"New Giza University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Saad","lastName":"Abdelnaby","suffix":""},{"id":607746759,"identity":"615db01c-0ca7-4fb3-8a6a-fb39a2b93e2c","order_by":3,"name":"Muhammad Ali Rushdi","email":"","orcid":"","institution":"New Giza University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Ali","lastName":"Rushdi","suffix":""},{"id":607746760,"identity":"e67285aa-d915-41a0-b33a-35b88f25cba5","order_by":4,"name":"Reem Saber Mohamed","email":"","orcid":"","institution":"New Giza University","correspondingAuthor":false,"prefix":"","firstName":"Reem","middleName":"Saber","lastName":"Mohamed","suffix":""},{"id":607746763,"identity":"46976ad4-34ef-4e8f-87ab-8a8e739f7e8d","order_by":5,"name":"Omar Mohammed Alhagrasi","email":"","orcid":"","institution":"New Giza University","correspondingAuthor":false,"prefix":"","firstName":"Omar","middleName":"Mohammed","lastName":"Alhagrasi","suffix":""},{"id":607746764,"identity":"297d259a-ac94-4bae-9829-9fc8e9166475","order_by":6,"name":"Mariam Megahed Tolba","email":"","orcid":"","institution":"New Giza University","correspondingAuthor":false,"prefix":"","firstName":"Mariam","middleName":"Megahed","lastName":"Tolba","suffix":""},{"id":607746765,"identity":"b2f16eef-3e47-4ae5-8ae8-094de1119af8","order_by":7,"name":"Dina Nabih Boulos","email":"","orcid":"","institution":"New Giza University","correspondingAuthor":false,"prefix":"","firstName":"Dina","middleName":"Nabih","lastName":"Boulos","suffix":""},{"id":607746769,"identity":"9a809f40-e322-4778-a3f1-86fe73b73fa6","order_by":8,"name":"Sherif Essam Eldeeb","email":"","orcid":"","institution":"New Giza University","correspondingAuthor":false,"prefix":"","firstName":"Sherif","middleName":"Essam","lastName":"Eldeeb","suffix":""}],"badges":[],"createdAt":"2026-03-09 15:53:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9075059/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9075059/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104996738,"identity":"7b109203-3ede-4786-9def-43a3b2372123","added_by":"auto","created_at":"2026-03-19 16:16:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1394518,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKnowledge regarding artificial intelligence among study participants\u003c/strong\u003e. A Stacked bar chart showing participants’ self-reported knowledge of artificial intelligence (AI) in medicine.\u003c/p\u003e","description":"","filename":"Figure1JPG.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9075059/v1/f8e8fbbbf6ac5fd97277be2c.jpg"},{"id":104996736,"identity":"fd16f34b-1a04-4d00-9e83-348963edca8f","added_by":"auto","created_at":"2026-03-19 16:16:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1895030,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePerception and attitude regarding artificial intelligence among study participants. \u003c/strong\u003eA Horizontal stacked bar chart depicting medical students’ perceptions and attitudes toward artificial intelligence (AI) in medicine.\u003c/p\u003e","description":"","filename":"Figure2JPG.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9075059/v1/085d6ddb0ef79bd64b6df05d.jpg"},{"id":104996735,"identity":"23c0321d-64ad-42cc-b0d7-efa2f2c3110d","added_by":"auto","created_at":"2026-03-19 16:16:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1324876,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMost useful ways for AI exploration according to study participants. \u003c/strong\u003eHorizontal bar chart showing study participants’ preferences for methods to explore artificial intelligence (AI) in medicine. The most endorsed methods included short lectures on AI fundamentals (63.47%) and workshops on programming AI models (60.47%).\u003c/p\u003e","description":"","filename":"Figure3JPG.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9075059/v1/17387dc0a9d02513d39ec4c3.jpg"},{"id":104996734,"identity":"03a349b2-6613-4fbd-8595-05abeb26b17b","added_by":"auto","created_at":"2026-03-19 16:16:09","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1768204,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePreferred topics about AI in medicine according to study participants. \u003c/strong\u003eA bar chart illustrating the preferred topics related to artificial intelligence (AI) in medicine.\u003c/p\u003e","description":"","filename":"Figure4JPG.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9075059/v1/9e2ca05fd26afbf3a1e09bad.jpg"},{"id":107065273,"identity":"e3b6b59f-4c8d-4035-85f0-4b22a9840a1c","added_by":"auto","created_at":"2026-04-16 10:57:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8439108,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9075059/v1/0cc1f186-4215-4c94-ae52-9d229e14473c.pdf"},{"id":104996732,"identity":"349f157c-343c-448f-9df3-cfcffe020753","added_by":"auto","created_at":"2026-03-19 16:16:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":846313,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFile name and format: Additional File 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFormat: PDF (Adobe Acrobat)\u003c/p\u003e\n\u003cp\u003eTitle of data: Assessment of Undergraduate Medical Students’ Perception and Knowledge Toward Artificial Intelligence and Its Applica on in Medicine among a Sample of New Giza University Students: A Cross-Sectional Study\u003c/p\u003e\n\u003cp\u003eDescription of data: The questionnaire used for this study.\u003c/p\u003e","description":"","filename":"Additionalfile1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9075059/v1/1ca8e6df8ad10481f11aeabc.pdf"},{"id":105035609,"identity":"ff20a309-a42e-4b5c-a166-84e8c16ee1ca","added_by":"auto","created_at":"2026-03-20 07:26:19","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28550,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFile name and format: Additional File 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFormat: Excel (.xlsx)\u003c/p\u003e\n\u003cp\u003eTitle of data: Supplementary materials for paper titled: “Assessment of Medical Students’ Perception and Knowledge Toward Artificial Intelligence and its Medical Applications among a Sample of New Giza University Students: A Cross-sectional Study.”\u003c/p\u003e\n\u003cp\u003eDescription of data: 6 tables including preferred time to study AI-topics in medicine and tables showing the relationship between students’ sociodemographics and their KAPs.\u003c/p\u003e","description":"","filename":"Additionalfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9075059/v1/99019ad8347199345486663d.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of Medical Students’ Perception and Knowledge Toward Artificial Intelligence and its Medical Applications among a Sample of New Giza University Students: A Cross-sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eArtificial intelligence (AI) is a computer system that enables machines to accomplish jobs that would ordinarily need human intelligence such as speech recognition, decision-making, and pattern recognition (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). AI technologies include primarily machine learning, deep learning, natural language processing, and computer vision (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). These different AI technologies have been steadily incorporated into medicine since the mid-1960s (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). However, recent years have witnessed a rapid integration that is reshaping multiple medical fields. For instance, in ophthalmology, AI systems like IDx-DR and EyeArt have been FDA-approved for screening diabetic retinopathy, achieving sensitivity and specificity rates exceeding 90% in real-world primary care settings (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In orthopedics, commercial fracture diagnosis tools like BoneView\u0026trade; have been shown to reduce diagnostic errors by up to 3 times in prospective studies (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Finally, in oncology, deep learning models such as 'LUMAS' have outperformed radiologists in predicting 1-year lung cancer risk from CT scans, achieving an AUC of 0.94 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese rapid advancements of AI applications in medicine not only enhanced the quality of healthcare services but also led to cost savings for healthcare systems(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). As such, in 2018, the American Medical Association adopted a policy called \u0026ldquo;Augmented Intelligence in Health Care\u0026rdquo;, to establish a foundation that helps AI grow and advance in healthcare. This policy necessitates the adequate preparation of medical students to get acquainted with and accept AI technologies as supporting tools in healthcare (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003eWhile the rationale behind including AI topics in medical education has been established, there is still a need to assess the level of knowledge and perception of medical students and their attitude towards integrating AI into their curricula to identify knowledge gaps and misconceptions that need to be addressed through targeted education. Studies from various countries have investigated the knowledge, attitude, and perception of medical students towards AI in the healthcare field (\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, existing literature predominantly focuses on knowledge assessment rather than providing actionable data for curriculum design.\u003c/p\u003e \u003cp\u003eEffective curriculum development, as articulated in Kern\u0026rsquo;s six-step framework, begins with a comprehensive needs assessment that identifies both perceived needs of learners and the actual gaps between current and desired competencies (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Therefore, this study aims to investigate the KAP of medical students towards AI and identify the specific content and methods that resonate with medical students. The analysis of the survey results will help identify knowledge gaps and misconceptions about AI, as well as establish an evidence base to inform the strategic integration of AI content within the medical curriculum.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis work introduces the results of a cross-sectional study that took place within the premises of New Giza University (NGU) (a multidisciplinary private university located in Giza governorate, Egypt). The data collection was conducted over a duration of approximately 5 months during the academic year 2024\u0026ndash;2025.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy tools and data collection\u003c/h3\u003e\n\u003cp\u003eThe study was based on a two-part questionnaire (Additional Files 1). The first part covers background and demographic information. The second part of the questionnaire was adapted (with permission) from a questionnaire conducted by Liu et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This part of the questionnaire assesses the students' current perception and knowledge of artificial intelligence (AI) in medicine. It also investigates the students\u0026rsquo; interest in learning more about AI, topics of students\u0026rsquo; interest in AI, and what resources students believe would be the most useful for their medical school to offer.\u003c/p\u003e \u003cp\u003eA pilot study was first conducted on a small group of 30 students to check the validity, consistency, and clarity of the survey questions. Those students gave valuable feedback to improve readability, flow, and understanding of the survey. Pilot participants were excluded from the final study sample.\u003c/p\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eThe recruitment process targeted undergraduate medical students at NGU. The inclusion criteria were to be enrolled as an NGU medical student in the academic year 2024\u0026ndash;2025 and at least finishing 1 academic year at the School of Medicine at NGU. Any survey respondents not satisfying these criteria were excluded.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSample size and sampling technique.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe appropriate sample size for the study was calculated to be 322, using EPI Info\u0026trade; StatCalc version 7. This number was set based on an expected total population of around 2000 students at NGU School of Medicine, an expected frequency of 50% given the highest sample size, and a 5% margin of error. Convenience sampling was employed to collect the survey responses. The study collected a total of 355 respondents, those with contradictory responses were manually detected and excluded. This left a total of 334 complete valid responses.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis.\u003c/h2\u003e \u003cp\u003eData analysis and visualization was done using IBM\u0026reg; SPSS\u0026reg; Statistics 27.0 and Microsoft\u0026reg; Excel\u0026reg; (version 2409), respectively. The 5-point Likert scale values were grouped into a 3-point Likert scale (agree, neither agree nor disagree, disagree) to facilitate clearer interpretation and reduce response variability. Chi-square tests were used to assess the significance of differences in students\u0026rsquo; KAPs based on their demographic variables. A p-value less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMedical students\u0026rsquo; demographics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (To be inserted above this paragraph) shows the sociodemographics of the participating medical students. The study involved 334 medical students at New Giza University with a mean age of 20.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.954 years. Gender distribution revealed that 55.4% of participants were females and 44.6% were males. The survey encompassed students from all stages of medical school- except for the first year. More than half of students\u0026rsquo; parents held a post-graduate degree, as opposed to a smaller proportion with a university degree, secondary education, or less.\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\u003eGeneral and Demographic Characteristics of the Study Participants (n\u0026thinsp;=\u0026thinsp;334)\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=\"char\" char=\".\" 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\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCount\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\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e149 (44.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e185 (55.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (Years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e160 (47.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e174 (52.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.954\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNationality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEgyptian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e331 (99.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Egyptian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (0.9%)\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 \u003cp\u003eYear 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100 (29.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52 (15.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56 (16.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear 5 (Final)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126 (37.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical practice experience / work shadowing / observership\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e173 (51.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161 (48.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFather\u0026rsquo;s educational degree\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114 (34.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e203 (60.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother\u0026rsquo;s educational degree\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150 (44.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e167 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAcademic background in tech field\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144 (43.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e190 (56.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdditional AI training in computer science\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e288 (86.2%)\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\u003eWhen addressing students\u0026rsquo; past experiences, 51.8% of participants had clinical practice experience. Meanwhile, 56.9% of responders had academic backgrounds in technologically oriented fields such as computer science, IT, robotics, or databases. However, 86.2% of participants had not received AI-focused training in computer science (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMedical students’ knowledge about AI\u003c/h3\u003e\n\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\u003eSources of AI Exposure Among Medical Students\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhere did you gain your exposure to AI?\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedia (YouTube, Twitter)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e264 (79.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTelevision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74 (22.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnline forums\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106 (31.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeer-reviewed articles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36 (10.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormal lectures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (9.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessors/doctors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (16.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBooks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35 (10.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50 (15.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProjects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50 (15.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConferences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39 (11.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily and friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e136 (40.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (0.3)\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\u003eThe survey responses revealed that 71.9% of students felt that they were unable to understand the fundamental concepts of AI (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, 44.1% of respondents stated that their medical school didn\u0026rsquo;t provide resources for topics about AI in medicine. Notably, 60.5% of participants reported that they were unable to cite recent clinically relevant AI-research, compared to 21.6% of participants who believed they could do so. Despite this, 71.3% of respondents stated they could list some benefits for the integration of AI in the medical field. Nearly 53.6% of respondents believed that it's hard to understand AI due to social media sensationalism. Also, 48.2% of respondents felt capable of distinguishing between \u0026lsquo;hyped\u0026rsquo; AI articles and those with true clinical significance, compared to 24.9% who felt they could not. Additionally, the study revealed that the majority of students acquired their knowledge about AI from media (79%), followed by family and friends (40.7%) and online forums (31.7%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Only a small proportion gained their understanding through formal lectures (9%) or from medical professionals (16.8%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMedical students’ perception and attitude towards AI\u003c/h3\u003e\n\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, 89.8% of participants agreed that AI will play a significant role during their lifetime and 71.2% were excited about incorporating AI into their future medical practices. However, 69.1% of the participants were concerned about the ethical implications of AI in medicine. Furthermore, 91% of respondents were interested in learning about AI in medicine and 87.7% acknowledged the incorporation of AI concepts into their medical education. In addition, 37.5% of students believed that integrating AI would not detract them from their core medical curriculum, while 28.8% expressed concerns about such detraction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePreferred modes and topics to explore AI\u003c/h2\u003e \u003cp\u003eUpon asking the participants about the most useful ways their university could offer to explore AI topics (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), they preferred short lectures (63.5%), followed by workshops (60.4%), and then symposia conducted by experts (36.5%). Moreover, 62.9% chose \u0026lsquo;When to use AI\u0026rsquo; as the most wanted topic, followed by \u0026lsquo;Strengths and weaknesses of AI in medicine\u0026rsquo; (53.6%), and then \u0026lsquo;Ethics of AI\u0026rsquo; (49.7%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In terms of time commitment, 57.3% of participants wanted to dedicate an average of 3 hours or less per month to learn about AI, while the remaining 42.5% wanted to spend more than 3 hours per month (Additional files 2, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePerceived effects of AI on medical specialties\u003c/h2\u003e \u003cp\u003eWhen considering medical specialty selection (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, to be inserted above this paragraph). The top-picked specialties were radiology (55.4%), followed by pathology (33.5%), interventional radiology (23.1%), and general surgery (23.1%). Furthermore, 33.6% of respondents were less likely to choose these selected specialties because of the anticipated integration of AI, while 31.2% were more likely to pursue such specialties- the rest remained neutral.\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\u003ePerceived Effects of AI on Medical Specialties According to Study Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\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\u003eWhat specialty do you wish to pursue in the future?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecialized surgery\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternal medicine\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObstetrics and gynecology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePediatrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRadiology\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003cb\u003e****\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI have not decided yet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhich specialties do you think will be most affected by AI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnesthesiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDermatology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmergency medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternal medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterventional radiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeurosurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeurology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObstetrics and gynecology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOphthalmology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrthopedic surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePediatrics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical medicine and rehabilitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlastic surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePsychiatry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRadiology (diagnostic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRadiation oncology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVascular surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI am less likely to choose these selected specialties because of the anticipated integration of AI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrongly agree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSomewhat agree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSomewhat disagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrongly disagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003e*\u003c/b\u003e\u003cem\u003eCardiothoracic Surgery, Neurosurgery, Orthopedic surgery, and Plastic surgery.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003e**\u003c/b\u003e\u003cem\u003eCardiology, Dermatology, Endocrinology, Gastroenterology, Hematology, Immunology, Nephrology, Neurology, Ophthalmology, Otolaryngology, and Respirology.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003e***\u003c/b\u003e \u003cem\u003eDiagnostic Radiology and Interventional Radiology\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003e****\u003c/b\u003e \u003cem\u003eAnesthesia, Emergency medicine, Pathology, Public, and Psychiatry.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDemographic factors associated with knowledge, attitudes, and perception.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe analysis of the study also compared how different independent variables are associated with students\u0026rsquo; KAPs. Regarding gender, more females (76.2%) were worried about the ethical considerations of using AI in medicine compared to males (60.4%) (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Additional file 2, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Also, 55.6% of females reported difficulty in understanding AI due to social media sensationalism, compared to 51% of males (p-value\u0026thinsp;=\u0026thinsp;0.03). No significant differences were observed between genders regarding understanding AI concepts, acceptance of AI integration in their formal medical education or future careers. Notably, students\u0026rsquo; academic years had no significant relationship with their knowledge, attitude or perceptions toward AI in medicine (Additional files 2, Table S3).\u003c/p\u003e \u003cp\u003eStudents with an academic background in technology-oriented fields (like computer science, IT, robotics) demonstrated significantly higher reports of knowledge about AI in medicine when compared to students without such background (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e to be inserted above this paragraph). However, there were no significant differences observed between the two groups regarding their perception of AI in medicine or their attitude towards integrating AI into their medical curricula.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between past technological background and participants\u0026rsquo; KAPs towards AI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStatement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLikert Response\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eAcademic background in technology-oriented fields\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;144 (100%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;190 (100%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\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\u003eAI will take on a significant role in medicine during my lifetime\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003cp\u003eNeither agree nor disagree\u003c/p\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132 (91.6%)\u003c/p\u003e \u003cp\u003e9 (6.2%)\u003c/p\u003e \u003cp\u003e3 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168 (88.4%)\u003c/p\u003e \u003cp\u003e12 (6.3%)\u003c/p\u003e \u003cp\u003e10 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI am excited about using AI tech as a future physician\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003cp\u003eNeither agree nor disagree\u003c/p\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (76.3%)\u003c/p\u003e \u003cp\u003e19 (13.1%)\u003c/p\u003e \u003cp\u003e15 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128 (67.3%)\u003c/p\u003e \u003cp\u003e42 (22.1%)\u003c/p\u003e \u003cp\u003e20 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI understand AI concepts like: CNN, Cross Validation, ROC AUC, Hyperparameters, Deep Learning, Hidden layers, etc.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003cp\u003eNeither agree nor disagree\u003c/p\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (18%)\u003c/p\u003e \u003cp\u003e27 (18.7%)\u003c/p\u003e \u003cp\u003e91 (63.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (10.5%)\u003c/p\u003e \u003cp\u003e21 (11%)\u003c/p\u003e \u003cp\u003e149 (78.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI can list some examples of recent clinically relevant AI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003cp\u003eNeither agree nor disagree\u003c/p\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (27%)\u003c/p\u003e \u003cp\u003e30 (20.8%)\u003c/p\u003e \u003cp\u003e75 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (17.3%)\u003c/p\u003e \u003cp\u003e30 (15.7%)\u003c/p\u003e \u003cp\u003e127 (66.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI can list the strengths/benefits of using Al in Medicine\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003cp\u003eNeither agree nor disagree\u003c/p\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111 (77%)\u003c/p\u003e \u003cp\u003e24 (16.6%)\u003c/p\u003e \u003cp\u003e9 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127 (66.8%)\u003c/p\u003e \u003cp\u003e36 (18.9%)\u003c/p\u003e \u003cp\u003e27 (14.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.044\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eI can list the weaknesses/pitfalls of using AI in medicine\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003cp\u003eNeither agree nor disagree\u003c/p\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (75%)\u003c/p\u003e \u003cp\u003e24 (16.6%)\u003c/p\u003e \u003cp\u003e12 (8.3%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130 (68.4%)\u003c/p\u003e \u003cp\u003e36 (18.9%)\u003c/p\u003e \u003cp\u003e24 (12.5%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43\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\u003eIt's hard to understand and approach AI because of media sensationalism\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003cp\u003eNeither agree nor disagree\u003c/p\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (52%)\u003c/p\u003e \u003cp\u003e49 (34%)\u003c/p\u003e \u003cp\u003e20 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (54.7%)\u003c/p\u003e \u003cp\u003e69 (36.3%)\u003c/p\u003e \u003cp\u003e17 (8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI can separate 'Hype' AI articles vs clinically relevant AI articles\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003cp\u003eNeither agree nor disagree\u003c/p\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (54.1%)\u003c/p\u003e \u003cp\u003e36 (25%)\u003c/p\u003e \u003cp\u003e30 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (43.6%)\u003c/p\u003e \u003cp\u003e54 (28.4%)\u003c/p\u003e \u003cp\u003e53 (27.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI am worried about the ethics of using AI in medicine\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003cp\u003eNeither agree nor disagree\u003c/p\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (72.2%)\u003c/p\u003e \u003cp\u003e26 (18%)\u003c/p\u003e \u003cp\u003e14 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127 (66.8%)\u003c/p\u003e \u003cp\u003e40 (21%)\u003c/p\u003e \u003cp\u003e23 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSome training on AI concepts and related topics during medical school can be useful for my future career\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003cp\u003eNeither agree nor disagree\u003c/p\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (88.1%)\u003c/p\u003e \u003cp\u003e15 (10.4%)\u003c/p\u003e \u003cp\u003e2 (1.396%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e166 (87.3%)\u003c/p\u003e \u003cp\u003e15 (8.4%)\u003c/p\u003e \u003cp\u003e8 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMy school offers resources if I want to explore the topic of AI in medicine\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003cp\u003eNeither agree nor disagree\u003c/p\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (22.9%)\u003c/p\u003e \u003cp\u003e45 (31.2%)\u003c/p\u003e \u003cp\u003e66 (45.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (15.2%)\u003c/p\u003e \u003cp\u003e80 (42.1%)\u003c/p\u003e \u003cp\u003e81 (42.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI want to learn what medical students should know about AI in medicine\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003cp\u003eNeither agree nor disagree\u003c/p\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134 (93%)\u003c/p\u003e \u003cp\u003e8 (5.5%)\u003c/p\u003e \u003cp\u003e2 (1.396%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170 (89.4%)\u003c/p\u003e \u003cp\u003e12 (6.3%)\u003c/p\u003e \u003cp\u003e8 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLearning the relevant topics of AI in medicine will significantly detract me from my medical school curriculum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003cp\u003eNeither agree nor disagree\u003c/p\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (25.6%)\u003c/p\u003e \u003cp\u003e47 (32.6%)\u003c/p\u003e \u003cp\u003e60 (41.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (31%)\u003c/p\u003e \u003cp\u003e66 (34.7%)\u003c/p\u003e \u003cp\u003e65 (34.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.343\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\u003eSimilarly, students who received additional training in computer science, particularly in AI-related topics, exhibited higher knowledge regarding AI in medicine (Additional files 2, Table S4). Finally, no significant differences were noted between parents\u0026rsquo; education and their children\u0026rsquo;s grasp of AI concepts, views on AI in medicine, attitude towards AI integration in their curricula or acceptance of AI in their future career (Additional files 2, Table S5-6).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLack of AI Knowledge Among Medical Students\u003c/h2\u003e \u003cp\u003eThe aim of this study was to assess the knowledge and perception of undergraduate medical students about AI and its integration into medical practice, as well as their preferences for incorporating AI\u0026rsquo;s education into their curriculum. The study included 334 medical students from their second year till their final year at NGU.\u003c/p\u003e \u003cp\u003eMost students in our study reported a lack of understanding of fundamental AI concepts (71.9%) and were unable to list recent AI advancements in the medical field (60.5%). This is in concordance with a recent study conducted on medical students in Jordan, where 58.9% of students lacked knowledge about AI applications in medicine (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Similarly a study conducted among medical students in India found that 52% of the students have heard about AI but possess no knowledge about its applications in medicine (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This relative lack of AI knowledge among students could be due to the insufficiency of AI-focused resources in medical curricula. In fact, the majority of students in our study reported that they were not provided with sufficient resources to explore topics of interest in AI. A similar concern was presented by a study conducted in Saudi Arabia, where 59.4% of medical students reported the lack of formal AI education in their university (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). This trend of inadequate and outdated resources might be due to the rapid developments of AI, which have outpaced the development of educational resources for medical curricula. Another study conducted in Egypt across 1346 medical students and house officers reported that the majority of students (78.3%) think they are knowledgeable about AI (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)- a sharp contrast to our findings. This discrepancy could be explained by the questioning approach used in their study, where they posed a broad query without providing concrete examples or defining the scope of AI knowledge. Such general questions may encourage overestimation of actual knowledge. This interpretation is supported by the participants\u0026rsquo; report of poor perception about AI and its applications in healthcare as well as their negative attitude towards AI (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). This discrepancy between AI perception and knowledge indicates their perceived knowledge did not translate into practical understanding of AI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRecognition of AI\u0026rsquo;s Importance\u003c/h2\u003e \u003cp\u003eOur study also found that most students recognized the importance of AI for the future of medicine (89.8%) and were interested in learning more about it (91%). This also aligns with a recent study conducted in an Egyptian public university, where over 80% of medical students believed that AI would revolutionize medical education, and over 85% showed interest in learning about AI applications in medicine (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Also, Al-Qerem et al. found that 69.5% of medical students in Jordan believed AI is a highly required tool in medicine, and 64.9% think AI should be part of their training system as medical students (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). These results reflect the growing recognition of AI\u0026rsquo;s importance among medical students, indicating the need for curricula to meet their demand for AI literacy. However, in another study in Egypt, the authors reported that 76.4% of participants\u0026rsquo; have inadequate perception of AI\u0026rsquo;s importance and usage in the medical field, with 87.4% expressing negative attitudes towards AI in healthcare (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). This distinction may be partly attributed to the participants\u0026rsquo; reliance on informal resources to gain their knowledge on AI, as 66.9% of participants reported that they acquired AI knowledge through self-study. Such an informal learning approach may provide inaccurate information, leading to misconception and skepticism about AI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eImpact of AI on Medical Specialty Selection\u003c/h2\u003e \u003cp\u003eWhen it comes to medical specialty selection, only 2.4% of students opted for radiology-related specialties. Furthermore, among those who chose radiology-related fields as one of the \u0026lsquo;top 3 specialties to be impacted by AI,\u0026rsquo; 37.5% agreed to the statement that they are less likely to choose any of these fields due to the expected AI integration, compared to 26.2% who disagreed with this statement. This relative avoidance towards radiology-related fields may stem from the common belief that AI integration in radiology could reduce demand for human expertise in these fields. In fact, Jackson et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) reported that most students expressed that their anxiety over potential \u0026ldquo;displacement\u0026rdquo; by AI technologies in radiology might deter them from pursuing this specialty. Such concerns could be plausible due to the rapid advancement of AI in radiology, which may even outperform human capabilities in certain cases (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). However, several recent studies have been increasingly supporting the view of AI as an adjunct to radiologists rather than a replacement. For instance, Purkayastha et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) demonstrated that AI-assistance radiologists achieved a significant reduction in diagnostic time and exhibited better diagnostic accuracy. This suggests that if physicians accept AI integration in their field, they may use AI to enhance their clinical impact, rather than view it as a competing force.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDemand for Formal AI Education\u003c/h2\u003e \u003cp\u003eThe analysis of our study also found that the students\u0026rsquo; main sources of knowledge about AI were media (79%), family and friends (40.7%), and online forums (31.7%), while only a minority gained their knowledge from formal lectures (9%), or from doctors and professors (16.8%). These results indicate that students rely mostly on informal resources rather than established academic studies. Such reliance could potentially lead to false perceptions or incomplete understanding of AI\u0026rsquo;s role in medical practice, especially in the light of the students\u0026rsquo; highly reported concerns regarding AI ethics in medicine. A study conducted on 1047 radiologists found that there is an inverse relationship between the level of fear among radiologists and their level of knowledge on AI, suggesting that proper training on AI-related topics could alleviate concerns and promote its use in clinical practice (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, although students currently rely on external, non-curricular resources for AI education, the majority of the respondents in our study (87.7%) think that introducing academic training on AI-related topics to be useful, while only few saw such a step as a detraction from the core medical curriculum. Similarly, a study conducted among medical and health science students across four Arab countries found that 51% of participants held a positive attitude toward integrating AI into health professional education, with Egypt representing the highest percentage within this group (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). These results suggest that including AI topics in formal medical education will be welcomed and embraced by most students. This is an important point to consider, especially in the light of the significant efforts required to overcome the challenges associated with implementing such integration effectively (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eRecommendations\u003c/h2\u003e \u003cp\u003eThe study findings highlight key considerations for designing AI curricula in medical education. Given that 57.3% of students preferred to dedicate three hours or less per month to AI education, we recommend that medical educators prioritize the seamless integration of AI topics into existing clinical and pre-clinical teaching to minimize curricular disruption.\u003c/p\u003e \u003cp\u003eStudents identified three priority topics: clinical indications for AI use (62.9%), strengths and limitations of AI in medicine (53.6%), and ethical implications (49.7%). These preferences reflect a demand for critical appraisal skills rather than technical programming knowledge. We recommend that curricula emphasize clinically oriented topics, enabling students to develop competency in evaluating AI tools' appropriateness, interpreting their outputs, and recognizing their limitations.\u003c/p\u003e \u003cp\u003eGiven the students' preference for short lectures (63.5%) and workshops (60.4%), it is recommended that curricular frameworks prioritize concise, active learning methodologies over traditional lengthy lecture formats to enhance engagement and critical thinking.\u003c/p\u003e \u003cp\u003eFinally, our finding that students may avoid radiology and related specialties due to AI- integration highlights the need to address misconceptions about AI's role. We recommend incorporating exposure to AI models within clinical rotations, where students observe radiologists and other specialists using AI tools to enhance diagnostic accuracy and efficiency. Such exposure may alleviate replacement fears by demonstrating AI's potential to augment rather than supplant physician expertise.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eA notable strength of the current study is that it stands, to the best of our knowledge, as one of the first to investigate the KAPs -as well as the preferred topics and modes of education- of AI among undergraduate medical students in Egypt. The study also offers a comprehensive comparison of the KAPs of students across different demographics (Additional files 2). Such analysis could highlight specific areas for a more personalized AI integration into medical curricula. Also, our study focused on students across all academic years in medical school except for first-year medical students who were just enrolled by the time the survey was conducted. Indeed, the medical knowledge and university experience at that time were probably insufficient for the first-year students to make sound judgment on the role of AI in medical education.\u003c/p\u003e \u003cp\u003eHowever, our study had some limitations that could be avoided in further studies. Firstly, the use of a cross-sectional design limits the ability to draw conclusions about the causation and the temporal relationships between variables. It also limits the depth of insights into students\u0026rsquo; true perceptions and attitudes. Secondly, the use of convenience sampling limits the generalizability of the findings, as the sample may not fully represent the broader population of medical students. This restricts the extent to which results can be applied across different institutions. Future studies employing probability-based sampling methods would provide a more comprehensive understanding of students\u0026rsquo; KAPs on AI. Finally, the study\u0026rsquo;s reliance on self-reported data introduces potential biases like recall bias, social desirability, or response bias. Alternative approaches such as conducting interviews could provide richer qualitative data (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eFuture Perspectives\u003c/h2\u003e \u003cp\u003eFuture research could benefit from incorporating objective performance-based assessments to evaluate students\u0026rsquo; knowledge of AI or proficiency in using AI tools. For instance, instruments such as the AI literacy test (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), AI literacy Concept Inventory (AI-CI) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), and SAIL-4-ALL scale (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e26\u003c/span\u003e) could be used to evaluate foundational AI literacy and skills in a standardized and structured manner. Beyond assessment, the next step for medical education is to conduct studies that evaluate frameworks designed for seamless and efficient AI integration. For example, the \u003cb\u003elow-dose, high-frequency (LDHF) model\u003c/b\u003e(27)\u0026mdash;which employs brief, interactive, and simulation-based learning activities that prioritize practical application and critical appraisal over technical programming: and the \u003cb\u003espiral curriculum framework\u003c/b\u003e (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), which distributes content longitudinally to introduce foundational concepts in pre-clinical years and revisits them with increasing complexity during clinical rotations. Investigating the effectiveness and longitudinal impact of these approaches will help identify best practices for implementation and ensure future physicians are equipped to navigate the clinical and ethical challenges of evolving healthcare settings\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results of the study revealed that most medical students lack knowledge and perception of AI in the medical field. However, they recognized the importance of AI for the future of medicine and showed interest in learning more about it. Students prefer concise, practical learning formats focused on clinical applications, ethical considerations, and the capabilities and limitations of AI. Integrating AI into medical education should extend beyond basic AI literacy. It should aim to cultivate future physicians who can critically appraise AI tools, understand their clinical applications and limitations, and navigate associated ethical challenges with confidence. The findings of this study can guide curriculum development amongst medical schools, as well as help modernize and improve medical education, ensuring that medical students are better prepared to use emerging technologies effectively in their medical practice.\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\"\u003eNLP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNatural Language Processing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKnowledge, Attitude, and Perception\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMRD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAge Related Macular Degeneration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPECT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle Photon Emission Computed Tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNGU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNew Giza University\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\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile Range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and consent to participate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the “New Giza Research Ethics Committee” at New Giza University (with approval code N-33-2024) to ensure compliance with ethical standards and participants’ confidentiality. Survey responses were collected anonymously, and no personal identifying information was collected. Study participant agreed on a consent form indicating the purpose of the study, statements on participant’s anonymity and confidentiality, and the estimated time for finishing the survey (5-7 minutes). This Informed consent was obtained from all participants prior to data collection. This study adheres to the ethical principles outlined in the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent\u003c/strong\u003e \u003cstrong\u003efor\u003c/strong\u003e \u003cstrong\u003ePublication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of Data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are in this published article and its supplementary material\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting\u003c/strong\u003e \u003cstrong\u003eInterests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Questionnaire preparation was performed by ME, YA, MA, RM, OH, and MT. All authors contributed to data collection. Data analysis was performed by ME and SE. The initial draft was prepared by ME, YA, SE, MA, and RM. Revision of the first draft was done by MR, ME , YA, MA, SE, RM, OH, MT, and DB. The work was supervised by SE, MR, and DB. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the assistance of Mohamed Ayman, Abdullah Karam, Ayman Elsayed, Mohamed Khater, and Omar Fouda with data collection. Also, the authors would like to acknowledge Dr. David Shalom Liu (College of Medicine and Life Sciences, University of Toledo, Toledo, OH, United States) and Dr. Bina Joe (Department of Physiology and Pharmacology, College of Medicine and Life Sciences, University of Toledo, Toledo, OH, United States) for allowing us to use their questionnaire.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eME\u003c/strong\u003e, MBBCH\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMedical Intern at Cairo University Hospital, received MBBCH from New Giza University in 2024.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFaculty of Medicine, New Giza University, Giza, Egypt.\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eORCID: 0009-0003-4008-7067\u003c/p\u003e\n\u003cp\u003eResearch interests: AI applications in medical education and healthcare, including machine learning applications in infectious diseases and non-interventional cardiology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYA\u003c/strong\u003e, MBBCH\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Medical Intern at Cairo University Hospital, received MBBCH from New Giza University in 2024.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFaculty of Medicine, New Giza University, Giza, Egypt.\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\u003cp\u003eORCID: 0009-0002-3474-2259\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMA,\u003c/strong\u003e MBBCH\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMedical Intern at Cairo University Hospital, received MBBCH from New Giza University in 2024.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFaculty of Medicine, New Giza University, Giza, Egypt.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Email: [email protected].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;ORCID: 0009-0009-2502-1557\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMR\u003c/strong\u003e, Ph. D.\u003c/p\u003e\n\u003cp\u003eA Full Professor with the School of Information Technology, New Giza University, and the Department of Biomedical Engineering and Systems in Cairo University. He received the B. Sc. Degree in biomedical engineering and systems in 2001, the B. Sc. Degree in mathematics from Cairo University in Egypt in 2003, the M. Sc. Degree in biomedical engineering and systems in 2005, and the M. Sc. And Ph. D. degrees in computer and information science and engineering from the University of Florida. Gainesville, FL, USA, in 2012 and 2013, respectively. His research interests include biomedical signal processing, medical imaging, information security and forensics, machine learning, image processing, computer vision, and applied mathematics\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\u003cp\u003eORCID: 0000-0001-9869-0270\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRM\u003c/strong\u003e, MBBCH\u003c/p\u003e\n\u003cp\u003eMedical Intern at Cairo University Hospital, received MBBCH from New Giza University in 2024.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFaculty of Medicine, New Giza University, Giza, Egypt.\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;ORCID: 0009-0001-5383-0775\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOA\u003c/strong\u003e, MBBCH\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMedical Intern at Cairo University Hospital, received MBBCH from New Giza University in 2024.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFaculty of Medicine, New Giza University, Giza, Egypt.\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\u003cp\u003eORCID: 0009-0001-9134-0136\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMT\u003c/strong\u003e, MMBCH\u003c/p\u003e\n\u003cp\u003eMedical Intern at Cairo University Hospital, received MBBCH from New Giza University in 2024.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFaculty of Medicine, New Giza University, Giza, Egypt.\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\u003cp\u003eORCID: 0009-0002-9581-4543\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDB, MD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProfessor and head of Public Health department, School of Medicine, at New Giza University. Received her MD of Public Health and Community Medicine in 2008 from Ain Shams University, Giza, Egypt.\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\u003cp\u003eORCID : https://orcid.org/0000-0002-9800-1751\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSE, MD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLecturer in School of Medicine, Public health department, at New Giza University. Researcher at Community Medicine Research Department, National Research Center. Received his MD in Public Health and Community Medicine in 2021 from Cairo University, Giza, Egypt.\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\u003cp\u003eORCID: https://orcid.org/0000-0002-9144-244X\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMukhamediev RI, Popova Y, Kuchin Y, Zaitseva E, Kalimoldayev A, Symagulov A, et al. Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges. Mathematics. 2022;10(15):2552. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/math10152552\u003c/span\u003e\u003cspan address=\"10.3390/math10152552\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSengar SS, Hasan AB, Kumar S, Carroll F. Generative artificial intelligence: a systematic review and applications. Multimed Tools Appl. 2024;84(21):23661\u0026ndash;700. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11042-024-20016-1\u003c/span\u003e\u003cspan address=\"10.1007/s11042-024-20016-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirani R, Noruzi K, Khuram H, Hussaini AS, Aifuwa EI, Ely KE, et al. Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities. Life. 2024;14(5):557. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/life14050557\u003c/span\u003e\u003cspan address=\"10.3390/life14050557\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalas M, Micieli JA, Wong JCY. Integrating AI with tele-ophthalmology in Canada: a review. Can J Ophthalmol. 2025;60(3):e337\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jcjo.2024.08.013\u003c/span\u003e\u003cspan address=\"10.1016/j.jcjo.2024.08.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElkohail A, Soffar A, Khalifa AM, Omar I, Mosaad M, Abdulaziz M et al. AI-Enhanced Surgical Decision-Making in Orthopedics: From Preoperative Planning to Intraoperative Guidance and Real-Time Adaptation. Cureus. 2025 Sep 19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7759/cureus.92762\u003c/span\u003e\u003cspan address=\"10.7759/cureus.92762\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang TG, Park S, Sch\u0026auml;ffer AA, Jiang P, Ruppin E. Hallmarks of artificial intelligence contributions to precision oncology. Nat Cancer. 2025;6(3):417\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s43018-025-00917-2\u003c/span\u003e\u003cspan address=\"10.1038/s43018-025-00917-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PubMed PMID: 40055572; PubMed Central PMCID: PMC11957836.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Leeuwen KG, Meijer FJA, Schalekamp S, Rutten MJCM, Van Dijk EJ, Van Ginneken B, et al. Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment. Insights Imaging. 2021;12(1):133. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13244-021-01077-4\u003c/span\u003e\u003cspan address=\"10.1186/s13244-021-01077-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaking Policy on Augmented Intelligence in Health Care. AMA J Ethics. 2019;21(2):E188\u0026ndash;191. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/amajethics.2019.188\u003c/span\u003e\u003cspan address=\"10.1001/amajethics.2019.188\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChakri I, El Khayali O, Lahlou L. Knowledge and Perceptions of AI Among Medical Students in Morocco: Cross-Sectional Study. JMIR Form Res. 2025;9:e66156\u0026ndash;66156. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/66156\u003c/span\u003e\u003cspan address=\"10.2196/66156\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmiri H, Peiravi S, Rezazadeh Shojaee SS, Rouhparvarzamin M, Nateghi MN, Etemadi MH, et al. Medical, dental, and nursing students\u0026rsquo; attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis. BMC Med Educ. 2024;24(1):412. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12909-024-05406-1\u003c/span\u003e\u003cspan address=\"10.1186/s12909-024-05406-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaupichler MC, Aster A, Meyerheim M, Raupach T, Mergen M. Medical students\u0026rsquo; AI literacy and attitudes towards AI: a cross-sectional two-center study using pre-validated assessment instruments. BMC Med Educ. 2024;24(1):401. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12909-024-05400-7\u003c/span\u003e\u003cspan address=\"10.1186/s12909-024-05400-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJackson P, Ponath Sukumaran G, Babu C, Tony MC, Jack DS, Reshma VR, et al. Artificial intelligence in medical education - perception among medical students. BMC Med Educ. 2024;24(1):804. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12909-024-05760-0\u003c/span\u003e\u003cspan address=\"10.1186/s12909-024-05760-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKern DE, editor. Curriculum development for medical education: a six-step approach. 2 ed. Baltimore, Md: Johns Hopkins Univ.; 2009. p. 253.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu DS, Sawyer J, Luna A, Aoun J, Wang J, Boachie, Lord, et al. Perceptions of US Medical Students on Artificial Intelligence in Medicine: Mixed Methods Survey Study. JMIR Med Educ. 2022;8(4):e38325. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/38325\u003c/span\u003e\u003cspan address=\"10.2196/38325\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Qerem W, Eberhardt J, Jarab A, Al Bawab AQ, Hammad A, Alasmari F, et al. Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions\u0026rsquo; students in Jordan. BMC Med Inf Decis Mak. 2023;23(1):288. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12911-023-02403-0\u003c/span\u003e\u003cspan address=\"10.1186/s12911-023-02403-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlabbad FA, Almeneessier AS, Alshalan MH, Aljarba MN. Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Saudi Arabia. J Fam Med Prim Care. 2025;14(4):1459\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/jfmpc.jfmpc_1812_24\u003c/span\u003e\u003cspan address=\"10.4103/jfmpc.jfmpc_1812_24\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllam RM, Abdelfatah D, Khalil MIM, Elsaieed MM, El Desouky ED. Medical students and house officers\u0026rsquo; perception, attitude and potential barriers towards artificial intelligence in Egypt, cross sectional survey. BMC Med Educ. 2024;24(1):1244. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12909-024-06201-8\u003c/span\u003e\u003cspan address=\"10.1186/s12909-024-06201-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhater AS, Zaaqoq AA, Wahdan MM, Ashry S. Knowledge and Attitude of Ain Shams University Medical Students towards Artificial Intelligence and its Application in Medical Education and Practice. Educ Res Innov J. 2023;3(10):29\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21608/erji.2023.306718\u003c/span\u003e\u003cspan address=\"10.21608/erji.2023.306718\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-019-1799-6\u003c/span\u003e\u003cspan address=\"10.1038/s41586-019-1799-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePurkayastha S, Isaac R, Veldandi A, Saxena R, Singh P, Vaswani P et al. Measuring Impact of Radiologist-AI Collaboration: Efficiency, Accuracy, and Clinical Impact. In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI) [Internet]. Athens, Greece: IEEE; 2024 [cited 2024 Nov 6]. pp. 1\u0026ndash;4. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ieeexplore.ieee.org/document/10635618/doi\u003c/span\u003e\u003cspan address=\"https://ieeexplore.ieee.org/document/10635618/doi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/ISBI56570.2024.10635618\u003c/span\u003e\u003cspan address=\"10.1109/ISBI56570.2024.10635618\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuisman M, Ranschaert E, Parker W, Mastrodicasa D, Koci M, Pinto De Santos D, et al. An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude. Eur Radiol. 2021;31(9):7058\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00330-021-07781-5\u003c/span\u003e\u003cspan address=\"10.1007/s00330-021-07781-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIssa WB, Shorbagi A, Al-Sharman A, Rababa M, Al-Majeed K, Radwan H, et al. Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey. BMC Med Educ. 2024;24(1):1166. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12909-024-06076-9\u003c/span\u003e\u003cspan address=\"10.1186/s12909-024-06076-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMasoumian Hosseini M, Masoumain Hosseini T, Qayumi K. Integration of Artificial Intelligence in Medical Education: Opportunities, Challenges, and Ethical Considerations. J Med Educ. 2024;22(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5812/jme-140890\u003c/span\u003e\u003cspan address=\"10.5812/jme-140890\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHornberger M, Bewersdorff A, Nerdel C. What do university students know about Artificial Intelligence? Development and validation of an AI literacy test. Comput Educ Artif Intell. 2023;5:100165. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.caeai.2023.100165\u003c/span\u003e\u003cspan address=\"10.1016/j.caeai.2023.100165\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Perry A, Lee I. Developing and Validating the Artificial Intelligence Literacy Concept Inventory: an Instrument to Assess Artificial Intelligence Literacy among Middle School Students. Int J Artif Intell Educ. 2024 May;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40593-024-00398-x\u003c/span\u003e\u003cspan address=\"10.1007/s40593-024-00398-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoto-Sanfiel MT, Angulo-Brunet A, Lutz C. The Scale of Artificial Intelligence Literacy for all (SAIL4ALL): A Tool for Assessing Knowledge on Artificial Intelligence in All Adult Populations and Settings [Internet]. SocArXiv; 2024 [cited 2024 Dec 3]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/bvyku\u003c/span\u003e\u003cspan address=\"https://osf.io/bvyku\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.31235/osf.io/bvyku\u003c/span\u003e\u003cspan address=\"10.31235/osf.io/bvyku\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJhpiego. Low dose, high frequency: a learning approach to improve health workforce competence, confidence, and performance [Internet]. Baltimore (MD): Jhpiego; 2013 [cited 2026 Feb 22]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hms.jhpiego.org/wp-content/uploads/2016/08/LDHF_briefer.pdf\u003c/span\u003e\u003cspan address=\"https://hms.jhpiego.org/wp-content/uploads/2016/08/LDHF_briefer.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarden RM. What is a spiral curriculum? Med Teach. 1999;21(2):141\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/01421599979752\u003c/span\u003e\u003cspan address=\"10.1080/01421599979752\" 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":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial intelligence, AI, Medical education, Knowledge, Perception, Attitude, Undergraduate, Medical students, Egypt, New Giza","lastPublishedDoi":"10.21203/rs.3.rs-9075059/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9075059/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe rapid integration of Artificial Intelligence (AI) into clinical practice necessitates preparing future physicians for such interaction. This study assessed medical students\u0026rsquo; knowledge, attitudes, and perceptions towards clinical AI and its integration into medical curricula and identified their preferred topics and modes of delivery for AI education.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional survey using a validated questionnaire was conducted with 334 undergraduate medical students at New Giza University, Egypt. Participants were questioned about their knowledge, attitudes, and perceptions (KAPs) toward AI in medicine, and their preferred AI topics and modes of education delivery. Chi-square testing analyzed associations between students\u0026rsquo; responses and their demographics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAnalysis of the responses revealed that 71.9% of students do not understand fundamental AI concepts, and 60.5% cannot cite recent clinical AI advancements. Furthermore, 69.1% express concern about AI\u0026rsquo;s ethical implications in medicine. Despite this, 89.8% recognize the importance of AI in the future of medicine and 91% desire further AI education. Preferred topics included when to use AI, strengths and weaknesses of AI, and ethics of AI. The preferred modes of education were short lectures, workshops, and symposia. No significant differences were found between students\u0026rsquo; KAPs and their academic year.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA substantial gap exists in medical students\u0026rsquo; knowledge and perception of AI in medicine; yet they strongly recognize its significance and are eager to learn about it for three hours or less per month. To address this, curricular developers should prioritize clinically oriented topics \u0026ndash; AI\u0026rsquo;s clinical applications, ethical implications, and the strengths and limitations - delivered in concise, interactive formats. Key learning outcomes must include the ability to critically appraise AI technologies, evaluate their outputs, and recognize limitations. Medical educators should embed these topics into existing teaching without overburdening students, cultivating future physicians confident in using AI and navigating its ethical challenges.\u003c/p\u003e","manuscriptTitle":"Assessment of Medical Students’ Perception and Knowledge Toward Artificial Intelligence and its Medical Applications among a Sample of New Giza University Students: A Cross-sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 16:16:04","doi":"10.21203/rs.3.rs-9075059/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"05db8a03-fc0c-473b-8fb2-a107b03f9b1e","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-16T10:56:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 16:16:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9075059","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9075059","identity":"rs-9075059","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}