Generative Artificial Intelligence Integration in Medical Education: A Cross-Sectional Survey of Medical Students’ Perceptions and Attitudes in Saudi Arabia

Generative Artificial Intelligence Integration in Medical Education: A Cross-Sectional Survey of Medical Students’ Perceptions and Attitudes in Saudi Arabia | 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 Generative Artificial Intelligence Integration in Medical Education: A Cross-Sectional Survey of Medical Students’ Perceptions and Attitudes in Saudi Arabia Fadi Aljamaan, Muhammad Faisal Mubarak, Ibraheem Altamimi, Alaa A. Alanteet, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6222830/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 evolution of Generative Artificial Intelligence (AI), particularly ChatGPT and large language models (LLMs), has introduced transformative potential in medical education. These tools offer innovative approaches to learning, simulation, and assessment. However, their integration into medical education remains underexplored, particularly in developing regions like Saudi Arabia. This study investigates medical students’ perceptions and attitudes toward AI in undergraduate medical education. Methods: A cross-sectional survey was conducted among 1,039 undergraduate medical students across Saudi Arabia. The survey, validated through pilot testing, assessed students' familiarity with AI, perceptions of its role in medical education, and acceptance of AI-driven teaching. Statistical analyses, including logistic regression, identified factors influencing students' perceptions. Results: Among participants, 57.2% were familiar with AI's role in medical education, and 70.1% supported integrating AI into their curriculum. Additionally, 86.4% believed AI would impact the future of medical education, and 71.1% felt access to AI chatbots would influence their competency. While 73.4% saw AI as beneficial for basic science education, only 41.6% recognized its potential for clinical training. Concerns included trust in AI-generated content (47.4%) and issues like reference fabrication (64%). Only 29.8% viewed AI as superior to traditional methods, yet 60.7% believed it would enhance academic performance. Conclusion: Saudi medical students show strong interest in AI integration, especially for basic sciences and simulation-based learning. However, they express skepticism about AI’s reliability and its ability to replace traditional tutor-based education. Concerns about ethical use and quality assurance highlight the need for structured guidelines to ensure AI is effectively incorporated while preserving critical human skills, clinical acumen, and ethical decision-making. Balancing AI with human instruction remains essential for its successful adoption in medical education. Artificial intelligence Medical education ChatGPT Undergraduate Medical students Saudi Arabia AI adoption. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Practice Points Saudi medical students widely support integrating AI into medical education, particularly in basic sciences. AI chatbots are valued as effective information sources but questioned for reliability and clinical skill training. Ethical concerns and fabricated references (hallucinations) are significant barriers to AI adoption. Students prefer human tutors over AI for clinical education. Structured guidelines are crucial for responsible AI integration. Glossary: Generative Artificial Intelligence (Generative AI): A type of artificial intelligence technology that creates original content by learning patterns from large datasets. It can generate text, images, audio, or other media, based on prompts provided by users. Examples include ChatGPT and similar large language models (LLMs). AI Chatbot Hallucination (Fabrication): A phenomenon occurring in generative AI systems, where the chatbot provides seemingly accurate but entirely fabricated or incorrect information, including references or sources. Hallucinations can mislead users into trusting false content, posing challenges for reliable integration in educational settings. AI-driven Assessment: The use of artificial intelligence technologies to evaluate learners' performance objectively, systematically, and without human bias. It involves automated grading, feedback, and competence measurement. While considered fair and efficient, concerns remain regarding transparency, accuracy, and alignment with educational objectives. Introduction During the last century, the landscape of medical care underwent a transformative evolution, from the development of mega size literature databases that spreads easily scholars’ achievements across the globe, to various technological and digital achievements that boosted medical care and outcomes in the last fifty years [1]. Medical education, which is the foundation source for producing capable healthcare professionals, has been affected by this revolution, too [2]. Educational domains like simulation, clinical skills and patient care training have been the major fields of evolution in that regard. The recent COVID-19 pandemic, with its lockdowns and social distancing measures, has significantly accelerated the adoption of telemedicine, virtual training, and online education [3]. The pandemic paved the road for rapid integration and advancements of artificial intelligence (AI) technologies as part of digital health that has been adopted and integrated broadly after the pandemic [4]. OpenAI's Chat Generative Pre-Trained Transformer (ChatGPT) is one of the pioneer advanced large language models (LLMs) that have been launched and stepped multiple evolutionary steps recently [5]. In medical education, this evolution is more than a technological leap; as it signifies a paradigm shift in how medical students and tutors’ access, interact with, and process medical and non-medical information [6] Notably, ChatGPT and similar LLMs are opening new dimensions in AI integration into healthcare. LLM technology shows promise in supporting the needs of learners and tutors, assisting in learner assessments, reviewing educational curricula, and ensuring alignment with educational objectives [7, 8]. As source of information, LLM has been shown to surpass conventional information sources like Google's search engine in terms of their output, summarization capability, organization and relevance to search questions [9, 10]. While preliminary studies have underscored ChatGPT’s potential in providing medical students with information across various specialties with variable reliability, there remains a critical need to ensure its standardization, credibility, integrity, and ethical uses [11–13]. Usage of AI chatbots like ChatGPT was associated with criticism in terms of their output’s credibility and information sources’ validity [14]. A study addressing ChatGPT implementation in medical education revealed its valued and efficient role in information gathering and summarization. However, concerns were noted about its content's critical depth, ambiguous sourcing and reliability, highlighting a need for a methodology to critically appraise its output and deal with ChatGPT in a balanced careful strategy [15]. Another valued potential of LLM in medical education is procedural and simulation education, multiple studies that assessed digital feedback on procedural skills training have shown promising learning outcomes [16, 17], while other studies also showed some negative outcomes that directed attention to protocolizing and objecting such computer assessed skills learning in order to improve outcomes and avoid any unintended disadvantages [18]. AI technology has been also implemented in learning objectives assessment with variable success [19–21]. The pace of AI development poses an urgent call for academics and medical educators to revisit and revamp their curricula and integrate these technologies in teaching methodology and assessment processes. It is imperative to adopt AI technology in teaching methods and enhance their outcomes, in addition to constructing ethics of conduct for their use among medical students, ensuring they are well-equipped for this rapidly evolving digital era [22, 23]. This paper proposes both immediate and long-term strategies, enabling medical educators to effectively steer through the challenges and opportunities presented in this escalating era of AI-chatbots and evolving LLMs. The primary objective of this study is to assess Saudi Arabian undergraduate medical students’ perception regarding the integration of AI technology within the educational framework in the initial stages of AI applications launch like ChatGPT. We also aimed to investigate medical students’ insights and perception of AI technology usage in the different domains of undergraduate medical education. The insights gained from this study will contribute to understanding the role of generative AI chatbots in shaping the future of medical education. Methods Study design Cross-sectional survey-based study targeting undergraduate medical students. The questionnaire used in this study was specifically developed by the research team to assess medical students’ perceptions and attitudes towards generative AI in medical education. An English version of the survey is provided as a supplementary file (see Supplementary File 1). The survey development process involved a comprehensive literature review and adoption from previous studies that targeted same or similar objectives. The work was followed by four focus group discussions (FGDs), to refine and finalize the survey based on expected domains of Generative Artificial Intelligence integration and employment in medical education, in addition to expected challenges and impact on medical education, patient care and healthcare sector in general. The final survey was reviewed by a multidisciplinary team comprising a pediatric intensivist professor (last author), an adult intensivist (first author), a medical educationist, a biostatistician and a group of undergraduate medical students at different levels (third, fifth and tenth to fourteenth authors). This diverse team of experts empowered the survey's robustness, by focusing on its content, validity and relevance. The refined survey was then pilot-tested with 30 medical students at different academic levels, ensuring its content validity, clarity and suitability. Feedback from this pilot study led to further refinement, enhancing the survey's overall structure and reliability. The survey was structured into three parts: The first part explored medical students' knowledge about generative AI technology and its usage in their educational context, particularly focusing on ChatGPT. Additionally, we explored the students’ perceptions of evolving AI technology’s potential future impact on medical education and their acceptance of adopting these technologies into their curriculum. The second part explored their perceptions and opinions about the role of generative AI in medical education, probing into its potential impact across various domains of medical education, i.e. basic sciences, clinical sciences, simulation, skill-based teaching and academic assessment and evaluation process. The final part gathered information on the participants' demographics and computer literacy. The questionnaire predominantly consisted of multiple-choice questions, encompassing a range of response types, and dichotomous questions (yes/no) with a neutral choice. This design allowed us to capture a comprehensive spectrum of responses. The survey was distributed among a diverse group of medical students, with the intention of covering the wide array of academic levels, within universities that have colleges of medicine in KSA. Participant Recruitment and Sampling Methodology To effectively gather data from a diverse range of medical students across Saudi Arabia, the research team adopted a digital-centric approach for survey distribution. The survey was hosted on SurveyMonkey for its proven efficiency in electronic dissemination and export of data for statistical analysis. The study was conducted over a four-week period from October 1 to October 30, 2023. Utilizing social media platforms widely used by the medical student community, such as X and WhatsApp, along with email invitations and personal contacts of the research team, the study aimed to ensure wide geographic coverage and inclusivity. The inclusion criteria consisted of any undergraduate medical students in any medical school within KSA. This method not only facilitated the recruitment of a broad spectrum of participants from various regions of Saudi Arabia but also aligned with the high engagement of medical students on these platforms, meeting the study's inclusion criteria. Sample size The required sample size was calculated using the Raosoft Sample Size Calculator [24]. The calculated sample size required was 386 medical students assuming estimated proportion of medical students using ChatGPT of 50%, margin of error of 5%, a confidence level of 95%, and a study power of 80%. To accommodate potential incomplete responses and non-responses, this number was increased by 20%, bringing the minimum required sample size to 463 medical students. Ethical Considerations This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. The study received ethical approval from the Institutional Review Board (IRB) at King Saud University, Riyadh, Saudi Arabia (Approval # E-23-7847). The purpose of the study was clearly outlined on the first page of the electronic survey, where informed consent was also obtained. For any queries, participants were given the option to contact the principal investigator via email. Participation in this study was entirely voluntary, and no financial or other incentives were provided to participants. Participants were informed that they could withdraw from the study at any time without facing any penalty or consequence. To maintain confidentiality, no personal identifiers were collected from the respondents. Statistical analysis Mean and standard deviations were used to describe continuous measured variables, frequencies and percentages for the categorically measured variables. The Kolmogrove-Smirnove (K-S) statistical test of normality and the histograms were used to assess the statistical normality assumption for metric variables. The Kuder-Richardson's test of internal consistency was used to assess the reliability of the binary measured questionnaire which was 0.71 for 16 tested dichotomous questions. The multivariable Logistic Binary Regression Analysis was applied to assess the statistical significance of participants’ measured binary dichotomized perceptions. The association between the predictor independent variables with the analyzed outcomes in the logistic regression analysis was expressed as Odds Ratios with their associated 95% confidence intervals. SPSS IBM statistical software version 28 was used for statistical data analysis. The alpha significance level was considered at 0.050 level. Results A total of 1039 medical students participated in our study with predominant male representation 64.3%. The participants split between junior medical students in basic science or non-clinical years 40.7% and senior students 47.3% in clinical years, while medical students in their internship year were 12%. The vast majority expressed very good to excellent computer skills 76.1%. The participants were 57.2% familiar with AI chatbots compared to 42.8% who were not. The vast majority admitted being able to access AI chatbots 75.5% (Table 1 ). Table 1 Descriptive Analysis of medical students sociodemographic and academic characteristics (N = 1039). Frequency Percentage Sex Female 371 35.7 Male 668 64.3 Study Level Junior students 423 40.7 Senior students 491 47.3 Intern 125 12 Participants’ computer skills Poor 29 2.8 Fair 220 21.2 Very good 516 49.7 Excellent 274 26.4 Familiarity with AI Chatbots role in medical education No 445 42.8 Yes 594 57.2 Accessibility to AI Chatbots No 255 24.5 Yes 784 75.5 As shown in Fig. 1 , 57.3% were familiar with AI role in medical education, while 86.4% of them believed AI will have future impact on medical education future Fig. 2 , and 70.1% agreed with incorporating AI technology in their medical school curriculum Fig. 3 . Participants’ perception of AI technology impact in medical education is shown in Table 2 , where 73.4% believed AI chatbots will have an impact on the basic sciences portion of medical education, followed by simulation-based teaching 58.1%, clinical sciences 54.9%, and only 41.6% with an impact on clinical skills teaching (Table 2 ). Table 2 Participants’ perception of AI implementation in medical education Frequency Percentage Which of the following is a potential impact of AI in medical education Clinical sciences (e.g. Internal medicine, OB/GYN) 570 54.9 Basic sciences (e.g. Physiology, Anatomy) 763 73.4 Simulation 604 58.1 Clinical Skills (e.g. Physical examination, surgical techniques) 432 41.6 Other (Assignments and Homework) 36 3.5 Do you think AI as a tutor would be more efficient and informative than a human tutor for basic science teaching? No 648 62.4 Yes 391 37.6 Do you think AI as a tutor would be more efficient and informative than a human tutor for clinical teaching, especially skills? No 831 80 Yes 208 20 Do you think AI based assessment versus human tutor would be fairer and more objective? No 502 48.3 Yes 537 51.7 Do you think medical students’ usage of AI Chatbots in assignments completion is ethical? No 519 50 Yes 520 50 Are AI Chatbots reliable sources of information for medical students? No 547 52.6 Yes 492 47.4 Are you aware of references fabrication/hallucination encountered with AI Chatbots? No 619 59.6 Yes 420 40.4 Is AI Chatbots’ references confusion/hallucination an obstacle for its use in medical education? No 374 36 Yes 665 64 Only 29.8% of participants believed AI technology will be superior to current traditional teaching methodology in medical schools, while 21.1% believed the opposite, and the rest were undetermined as shown in Fig. 4 . The vast majority (80%) did not think ‘AI-harnessed medical teaching technology’ would be more informative than ‘traditional human tutor’ for clinical sciences and 62.4% did not think so for basic sciences as shown in Table 2 . On the other hand, 51.7% believed that ‘AI assisted academic assessment’ would be fairer and more objective compared to ‘human tutoring.’ The participants split equally regarding their perception of ethicality using AI chatbots in their assignments. Whereas 60.7% of the participants believed that AI technology will improve their academic achievements, while 9.3% believed there was a lack of effect in this, and the rest were undetermined (Fig. 5 ). Only 47.4% believed AI chatbots as reliable sources of information for medical students. In the same context, 40.4% were aware of AI chatbots references’ fabrication/hallucination with 64% of participants perceiving this fallacy to pose an obstacle of incorporating them into medical education (Table 2 ). We also assessed participants’ perception of AI impact on the future of medical practice as shown in Table 3 and we found 71.1% believed that physicians’ access to AI will affect their future competencies and efficiencies. While 44.7% believed it might lead to the replacement of some medical specialties in the future, 33.8% did not believe so, and 21.6% were undetermined in that regard. On the other hand, 61.1% believed AI technology will improve medical practice and patient care outcomes. Table 3 Participants’ perception about AI and future of medical practice Frequency Percentage Do you think physician access to AI Chatbots in the future would affect their competencies and efficiency? No 300 28.9 Yes 739 71.1 Do you think AI technology will improve medical practice? No 115 11.1 Neutral 300 28.9 Yes 624 60.1 Do you think AI technology will improve patients’ care? No 131 12.6 Neutral 284 27.3 Yes 624 60.1 Do you think AI technology might replace some of the medical specialties in the future? No 351 33.8 Neutral 224 21.6 Yes 464 44.7 Table 4 shows multivariate binary logistic regression analysis of the participants’ characteristics that are associated with their perception of positive impact of AI technology on the future of medical education. Males were significantly less inclined to believe in the positive impact of AI technology on medical education future compared to females (39% times less, p-value = .028). Medical students who agreed with superiority of AI compared to traditional tutors in terms of clinical teaching efficiency and informativity were significantly less inclined to believe in its future positive impact on medical education (50.4% times less, p-value = .006). However, students who encouraged the incorporation of AI technology in medical schools teaching methods, and those who believed AI chatbots are reliable source of medical information, were significantly more inclined to believe in their positive impact on medical education future (3.83 times more, p-value < .001, 1.926 times more, p-value = .003) respectively. On the other hand, students who believed AI chatbots can improve medical students’ academic achievement were significantly less inclined to believe in AI technology positive impact on the future of medical education (23.5% times less, p-value = .020). Similarly, those who believed AI technology will improve medical practice or patients’ care were significantly less optimistic of its positive impact on the medical education future (40.3% times less, p-value < .001, 23.5% times less, p-value = .027), respectively. Table 4 Multivariable Logistic Binary Regression Analysis of participants’ belief in the impact of AI technology on medical education future Multivariate adjusted odds Ratio OR 95% C.I. p-value Lower Upper Sex 0.610 0.393 0.948 0.028 Age (years) 0.978 0.895 1.069 0.627 AI more efficient and informative for clinical teaching 0.496 0.300 0.819 0.006 Encourages AI incorporation in medical teaching methodology 3.583 2.401 5.347 < 0.001 AI Chatbots are reliable source of information for medical student 1.926 1.244 2.980 0.003 AI would improve medical students’ academic achievement 0.765 0.610 0.959 0.020 AI will improve medical practice 0.597 0.466 0.766 < 0.001 AI will improve patients’ care 0.764 0.602 0.970 0.027 Constant 45.762 0.000 DV: Participants’ belief in the impact of AI technology on medical education future Table 5 shows the multivariable binary logistic regression analysis for the odds of participants’ characteristics associated with their agreement to incorporate AI technology in medical education methodology. Male medical students compared to females were significantly more disagreeing with incorporating AI technology in medical education methodology (32.4% times less agreeing, p-value = 0.022). While senior medical students (22 years or older) compared to others were significantly more agreeing with incorporating AI technology in medical education methodology (1.57 times more, p-value = 0.003). Medical students who had access to AI chatbots were significantly more agreeing to incorporate AI technology in medical schools teaching methodology (59.5% p-value = 0.005). Medical students who believed in AI chatbots’ usefulness in medical simulation-based teaching, and its superiority to human tutor for teaching basic science, were significantly more in agreement in incorporating them into medical schools teaching methodology (1.439 times more, p-value = 0.015, 2.117 times more, p-value < 0.001) respectively. Medical students who believed that AI-assisted academic assessment would be fairer and more objective than human evaluators were significantly more agreeing in incorporating AI technology in medical schools teaching methodology (1.667 times more, p-value = 0.001) The medical students who believed that using AI chatbots in assignment completion is ethical were significantly more in agreement in incorporating this technology in teaching methodology (1.593 times more, p-value = 0.002). On the other side, those who believed that AI chatbots usage would enhance their academic achievement were significantly less agreeing to incorporate AI technology in education methodology (28.3% times less, p-value < 0.001). Medical students who perceived AI technology will improve patients’ outcomes were significantly less agreeing to incorporate it in their medical schools teaching methodology (24.4% times less, p-value = 0.001). Table 5 Multivariable Logistic Binary Regression Analysis of participants’ agreement with AI incorporation in medical school education methodology Multivariate adjusted Odds Ratio OR 95% C.I. p-value Lower Upper Sex 0.676 0.484 0.946 0.022 Age Group 1.572 1.163 2.123 0.003 University location 1.419 0.911 2.210 0.122 AI Chatbots ease of access 1.595 1.152 2.209 0.005 AI has a potential impact in simulation-based teaching 1.439 1.073 1.929 0.015 AI more efficient and informative for basic sciences 2.117 1.531 2.927 < 0.001 AI based academic assessment would be fairer and more objective 1.677 1.247 2.256 0.001 Medical students’ usage of AI Chatbots in assignments completion is ethical 1.593 1.185 2.142 0.002 AI would improve medical students’ academic achievement 0.717 0.609 0.844 < 0.001 AI will improve patients’ care 0.756 0.640 0.893 0.001 Constant 1.844 0.070 DV: participants’ agreement with AI incorporation in medical school education methodology Table 6 sheds light on the participating medical students’ characteristics associated with their belief in AI chatbots’ reliability as medical information source. Male medical students compared to females had significantly lower belief (31.6% times less, p-value = 0.008). Those who had strong belief in AI role in simulation teaching had significantly less belief in its information source reliability (24.1% times less, p-value = 0.048). While those who believed AI would be more efficient than human tutors in basic science, clinical skills teaching and is potentially a fairer evaluator, had significantly higher trust in AI medical information reliability (66.5%, p-value = .001, 82.4%, p-value = .002, 36.2%, p-value .029) respectively. Medical students who believed using AI chatbots in assignment completion is ethical had significantly higher belief in their medical information reliability (63.2%, p-value < .0010). On the other hand, those who expected AI will have positive impact on medical education future had significantly less belief in AI chatbots medical information reliability (21.4% times less, p-value = .003). Medical students’ belief that AI might have an impact on the future of medical education correlated significantly and positively with their belief in its information reliability (223%, p-value < .001), but those who believed this impact is due to its superiority compared to traditional medical education methods had less trust in its medical information reliability (20% times less, p-value = .006). However, medical students’ belief that AI chatbots’ hallucination would be an obstacle for its incorporation in medical education had less belief in its medical information reliability (33.2% times less, p-value = .004) Table 6 Multivariable Logistic Binary Regression Analysis of medical students’ odds believing in the reliability of AI chatbots as source of medical information Multivariate adjusted Odds Ratio OR 95% C.I. p-value Lower Upper Sex 0.684 0.518 0.904 0.008 Age(years) 0.979 0.922 1.039 0.477 AI usefulness in medical simulation (Clinical scenarios) 0.759 0.577 0.998 0.048 AI would be more efficient than human tutor for basic science teaching 1.665 1.242 2.233 0.001 AI would be more efficient than a human tutor for clinical skills teaching 1.824 1.257 2.647 0.002 AI based academic assessment would be fairer and more objective 1.362 1.033 1.796 0.029 Medical students’ usage of AI Chatbots in assignments completion is ethical 1.632 1.246 2.137 < 0.001 AI Chatbots would impact medical education future 2.237 1.450 3.453 < 0.001 AI Chatbots references hallucination is an obstacle for its application in medical education 0.662 0.501 0.874 0.004 AI will improve medical practice 0.786 0.670 0.921 0.003 Constant 1.419 0.636 DV: Participants’ belief in AI chatbots as reliable source for medical education Discussion Our study is empowered by including large number of medical students 1039. Most of them (75.5%) admitted to being able to access AI chatbots, yet they were less familiar (57%) with areas where it could be applied in medical education. Our decent number of participants’ familiarity with AI chatbots correlates with similar literature findings ranging from 50–85% [25–28]. This low curiosity and interest at the current stage of incorporating Generative AI technology in medical education, points to medical students’ hesitancy and reluctance toward AI role in medical education so far. At the same time, the majority (86.4%) of participants believed of AI inevitable future impact on medical education as supported by similar surveys [29]. In comparison to our studied cohort, Jordanian medical students, who are very close geographically, sociologically, and technologically to our population, had lower expectation (67%) of AI’s potential to revolutionize medical education [27]. Participants high expectations of AI’s future impact on medical education and encouraging its incorporation in medical education, this might be driven by their expectation of its multidimensional tutorship efficiency including basic sciences, simulation teaching to some extent and their potential role to improve students’ academic achievement, that mirrors similar survey studies regarding medical students’ optimism in terms of AI technology application in medical education and assignment preparation [27]. At the same time, the participants had an inconsistent vision regarding AI technology incorporation in medical education, especially when they were questioned if it will have any long-lasting future impact when compared to traditional teaching methodology. Those who believed AI application in medical education would improve clinical practice and patient care outcomes, did not support incorporating AI technology in medical education, this contradiction might be explained by their perception of AI technology role more befitting for administrative, logistical, and organizational roles rather than educational applications [30], or because they perceived academics and students’ skills are still lacking and precluding AI application into medical education as observed similarly in a previous survey [28]. While we observed that participants who had strong belief in AI technology’s positive impact on basic sciences, simulation teaching, and as an academic assessment tool, they had strong expectations and acceptance of incorporating AI technology in medical education. Interestingly, survey takers who believed AI technology would improve their academic achievements, were not supportive of incorporating it into medical education methodology and did not believe it would have an impact in that regard. Thus, limiting their expectations of AI technology to being a source of information, assistive and organizing tool rather than promising for a major revolutionary role in medical education. These positive attitudes in general but with different reservations mirror findings from multiple studies as shown in systematic review by Chen et al, in 2022 of cross-sectional surveys targeting physicians and medical students regarding their acceptance of clinical applications of AI [31]. Noteworthy, 71.1% of participants believed that physicians’ access to AI chatbots will affect their future clinical and career competency, but did not expect positive impact of AI technology on patients’ care and medical practice, that is explained in similar surveys that showed physicians and medical students reservation about clinical artificial intelligence impact on their competencies due to the complexity of the medicine field that requires human physician approach not yet achieved by clinical AI [28]. Also, they did not expect AI technology might replace some medical specialties in the future, Chen systematic review has shown similar findings, there was consensus about the need for collaboration between clinical AI and human physician, in spite of mixed opinions about its future as a surrogate physician [31]. Therefore, medical students and physicians are encouraged to build up and optimize their AI technology competency to keep up with future healthcare systems that will inevitably incorporate clinical Generative AI technology in different sectors [32]. Female students compared to male students had stronger belief of AI technology impact on the future of medical education, encouraged incorporating it in medical education curriculum, and trusted AI chatbots to be a reliable source of medical information. The literature showed inconsistent findings regarding sex differences in terms of medical and allied healthcare professional students’ optimism for AI future and its impact on medical education [33–37]. 70.1% of the students surveyed agreed with incorporating AI technology in their medical school curriculum. However, when questioned about AI technology’s possible superiority compared to traditional teaching methodology, only 29.8% expected it to be superior and 21.1% expected AI technology to be inferior in this regard, but the majority were undetermined. At the same time, the medical students showed inconsistent or reluctant vision for AI impact of different medical education domains especially when compared to traditional human tutors, as they were optimistic of its role for basic sciences education, because they believed AI chatbots to be good sources of information and supported its superiority and had strong expectations of its impact on medical education. While to a lesser extent, participants had less enthusiasm for AI tutor role in comparison to human tutor for simulation and clinical medicine teaching. This doubtful optimism is probably explained by the students’ perception of AI’s role at the current stage as an information source, that summarized and organized the educational material well and presented it in a concise format, rather than a tutoring or mentoring tool and highlights their appreciation of human tutor role in medical education process and their unique skills that cannot be replaced so far by artificial intelligence technology. The effectiveness of human tutorship versus AI tutorship is an ongoing debate in literature with most literature emphasizing the pivotal role of human expert tutorship as being more effective in implanting professionalism and critical thinking skills in medical students. This subset of survey takers perceived certain skills including critical appraisal of existing clinical evidence to be of inherent humanistic skills exclusively [38, 39], Current literature supports concerns over AI application and medical education in relationship to advertent or inadvertent overreliance on AI applications by medical students, evanescence of critical thinking skills and the reliability of LLM output [14]. For the sake of fairness, AI has been accepted widely for its tutoring skills, especially in the field of surgical skills teaching [40]. One of the possible challenges of incorporating AI chatbots into medical education is lack of information reliability. Only 47.4% of our participants believed AI chatbots will act as a reliable source of information and predicted a high impact on medical education in the future but perceived it to be lacking in the concurrent time. Another challenge is the widely identified phenomenon in the medical literature of information and references’ fabrication/hallucination encountered while utilizing AI applications [41–43], as 64% of survey takers believed it might be an obstacle for AI chatbots incorporation in medical education. Those who were concerned about AI chatbots hallucination had negative expectations regarding their effect on physicians’ future competencies and perceived these hallucinations to be an obstacle to AI incorporation in medical education. Interestingly, these concerns were not associated with significant concerns of AI’s future impact on clinical practice and patient care. Senior medical students (medical students in their 3rd to 5th years of medical school) had lower expectations that AI technology would affect physicians’ future competencies and efficiency in comparison to basic science students (medical students in their 1st and 2nd years of medical school), this might be related to senior medical students advanced level especially given their exposure to clinical medicine practice. This notion underscores the human factor in favoring AI technology or machine-driven competency in similar observation reported in literature linking students’ knowledge base level and degree of optimism of AI applications in Medicine [36]. 44.7% of our participants expected AI technology to replace some medical specialties in the future albeit not carrying the perception of clinical AI having an adversary effect on their future clinical competency or proficiency. This observation might be explained by perceiving AI technology to be inferior to human competency due to human’s cumulative training, acquired clinical critical thinking skills, and humanistic approach to patients. Our findings streamline reviewed contextual literature that AI will only act to complement and reinforce human physician skills rather than replace them [44, 45]. While regarding fairness and objectivity of assessment and evaluation process, participants split equally in their expectation of AI technology in comparison to human tutoring. Students who perceived AI to be fairer and more objective in academic assessment compared to human tutors strongly encouraged its incorporation in medical education methodology and had higher trust in it as source of medical information. Furthermore, the same group of students had high expectations of its impact on medical education and healthcare practice. This trust in AI technology assessment skills has been observed in the literature and credited as an advantageous characteristics for its implementation in education field [46]. The same group of students also had strong belief in AI chatbots’ effect on physicians’ future competency and clinical efficiency. This extreme observation might stem from biased unrealistic expectations of applied AI role in medical education and medical practice without substantive basis. This point is made as previous studies using AI in neurosurgical training showed encouraging results with AI tutoring via feedback in terms of positive emotions [40]. Medical students may perceive AI-administered evaluations free from the personal biases that human evaluators might encounter [46]. However, similarly, AI technology might introduce another element of bias, which is dependent on algorithms and protocols programmed to feed AI platforms during prompting and evaluation processes. Therefore, AI implementation in medical education assessment should be protocolized objectively, skeptically and employed carefully to achieve totalitarian unbiased assessment process. Students who believed in AI’s role in simulation and basic science teaching in comparison to clinical teaching, had strong inclination for incorporating AI in medical education and expected positive impact on the medical education future. Participants who believed in AI’s superiority in tutoring clinical and basic sciences teaching had a strong belief in AI chatbots’ information reliability compared to those who encouraged its implementation in simulation teaching who believed more in AI technology assistive and demonstrating role. These findings show a dichotomous perception among survey takers with a group believing strongly in AI application for information resourcing and being supportive of AI adoption in education methodology while the other group perceived AI usefulness in the virtual and robotic applications of AI as information resourcing and diagnostic tools. Interestingly, the participating students who believed in AI’s potential impact on medical education held a negative perception of AI’s ability to improve patient care and clinical outcomes. This viewpoint seems contradictory to the overall optimism expressed by 60% of the participants, who felt that AI technology would positively affect patient care and medical practice. A plausible explanation to this contradiction could be explained by participants perception of different AI role in medical education in comparison to medical practices and its outcomes, and the participants’ immature understanding of the clinical care process and healthcare systems as various AI applications in the medical field have proven to improve overall patient care especially when it comes to medical decision making in complex cases, inventing personalized therapies for patient with frequent hospital admissions, aiding surgeons during operation, and fine-tuning infection control within a healthcare system [9]. : Limitations Our survey inherently has potential limitations. It was launched early when generative AI applications had just started their adoption in medical practice. The participants’ humble familiarity with AI applications may have limited their insights about the future of AI technology in medical education and healthcare sectors, though it did give early cross-sectional input on their early experience and anticipations. Our survey did not dig deep into the medical students’ understanding of deep learning methodology of AI technology and the evolution of LLM which would have extended the survey and made it cumbersome to complete. We addressed medical students’ prospects regarding AI technology and medical education and did not assess their tutors. Our survey was conducted early in the adoption of generative AI in medical education, which may have limited participants' insights into its long-term impact. Their modest familiarity with AI applications at the time could have influenced their perceptions. Additionally, we did not explore students' understanding of deep learning or LLM evolution, nor did we assess educators' perspectives, which are crucial for AI integration. Future research should investigate faculty viewpoints, assess AI’s long-term impact on medical training, and explore the effectiveness of advanced AI models like GPT-o1 or DeepSeek and other domain-specific LLMs in clinical education and decision-making [32]. Conclusion Medical students expressed strong encouragement of incorporating AI technology into their medical school curricula, with a strong belief in AI capability for enhancing their academic achievements. AI applications have promising roles such as medical information resources, enriching simulation teaching like surgical skills and optimizing basic sciences teaching in anatomy and physiology for example. Medical students demonstrated awareness of human tutor superiority over current AI applications and expressed awareness of serious concerns in relation to potential misuse and overreliance on AI applications. However, a minority of them expressed unrealistic overzealous expectations with the technology that warrants attention by academics soon. In our study, most medical students utilized AI applications as information sources, especially that many AI applications showed superiority in summarizing and organizing medical literature. Other serious concerns were expressed by medical students regarding AI chatbots used in medical literature search protocols resulting in fabrication of information and references. There is an urgent and ongoing need in addressing the accuracy of AI applications’ outputs in the medical field, to maturate and optimize their implementation in the medical education process. Declarations Ethics Approval: Institutional Review Board (IRB) at King Saud University, Riyadh, Saudi Arabia (Approval # E-23-7847) Clinical trial number Not applicable. Funding: None. Declaration of Interest : The authors declare no conflicts of interest related to this study. Data Availability Statement : The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request Author Contribution Author Contributions Statement: F.A., M.F.M., I.A., M.S., and M.-H.T. conceptualized the study and drafted the Methods and Results sections. A.A.A., M.A.A., and S.A.D. contributed to drafting the Discussion section. R.A., M.I.A., A.A.S., A.O.A., A.A.A., S.H.A., B.N.A., and S.I.A. were responsible for drafting the Introduction, preparing the IRB proposal, and conducting data collection. K.S.B. and A.J. critically revised the manuscript, integrating insights on medical education themes and medical informatics. All authors contributed to data interpretation, manuscript revision, and approved the final version for submission. Additionally, all authors agree to be accountable for all aspects of the work, ensuring accuracy and integrity. Acknowledgement The authors thank the participating medical students and King Saud University for supporting this research. The authors acknowledge the use of ChatGPT-4o to refine the language and enhance the clarity of this manuscript. However, the final text was thoroughly reviewed and edited by the authors, who take full responsibility for its content and accuracy. Data Availability Data Availability Statement: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. References Singh S, Bhatt P, Sharma SK, Rabiu S: Digital transformation in healthcare: Innovation and technologies . In: Blockchain for Healthcare Systems. edn.: CRC Press; 2021: 61–79. Jamal A, Solaiman M, Alhasan K, Temsah M-H, Sayed G: Integrating ChatGPT in Medical Education: Adapting Curricula to Cultivate Competent Physicians for the AI Era . Cureus 2023, 15 (8). Chiesa V, Antony G, Wismar M, Rechel B: COVID-19 pandemic: health impact of staying at home, social distancing and ‘lockdown’measures—a systematic review of systematic reviews . Journal of public health 2021, 43 (3):e462-e481. Metcalf CJE, Lessler J: Opportunities and challenges in modeling emerging infectious diseases . Science (New York, NY) 2017, 357 (6347):149–152. Shoja MM, Van de Ridder JMM, Rajput V: The Emerging Role of Generative Artificial Intelligence in Medical Education, Research, and Practice . Cureus 2023, 15 (6):e40883. Stoumpos AI, Kitsios F, Talias MA: Digital Transformation in Healthcare: Technology Acceptance and Its Applications . Int J Environ Res Public Health 2023, 20 (4). Chan KS, Zary N: Applications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review . JMIR Med Educ 2019, 5 (1):e13930. Khamis N, Chen B, Egan C, Gaglani S, Tackett S: More intelligent faculty development: Integrating GenAI in curriculum development programs . Med Teach 2025:1–3. Haleem A, Javaid M, Khan IH: Current status and applications of Artificial Intelligence (AI) in medical field: An overview . Current Medicine Research and Practice 2019, 9 (6):231–237. Breeding T, Martinez B, Patel H, Nasef H, Arif H, Nakayama D, Elkbuli A: The Utilization of ChatGPT in Reshaping Future Medical Education and Learning Perspectives: A Curse or a Blessing? Am Surg 2024, 90 (4):560–566. Temsah O, Khan SA, Chaiah Y, Senjab A, Alhasan K, Jamal A, Aljamaan F, Malki KH, Halwani R, Al-Tawfiq JA et al : Overview of Early ChatGPT's Presence in Medical Literature: Insights From a Hybrid Literature Review by ChatGPT and Human Experts . Cureus 2023, 15 (4):e37281. Fatima A, Shafique MA, Alam K, Fadlalla Ahmed TK, Mustafa MS: ChatGPT in medicine: A cross-disciplinary systematic review of ChatGPT's (artificial intelligence) role in research, clinical practice, education, and patient interaction . Medicine (Baltimore) 2024, 103 (32):e39250. Abouammoh N, Alhasan K, Aljamaan F, Raina R, Malki KH, Altamimi I, Muaygil R, Wahabi H, Jamal A, Alhaboob A et al : Perceptions and Earliest Experiences of Medical Students and Faculty With ChatGPT in Medical Education: Qualitative Study . JMIR Med Educ 2025, 11 :e63400. Benítez TM, Xu Y, Boudreau JD, Kow AWC, Bello F, Van Phuoc L, Wang X, Sun X, Leung GK, Lan Y et al : Harnessing the potential of large language models in medical education: promise and pitfalls . J Am Med Inform Assoc 2024, 31 (3):776–783. Abouammoh N, Alhasan K, Raina R, Malki KA, Aljamaan F, Tamimi I, Muaygil R, Wahabi H, Jamal A, Al-Tawfiq JA et al : Exploring Perceptions and Experiences of ChatGPT in Medical Education: A Qualitative Study Among Medical College Faculty and Students in Saudi Arabia . medRxiv 2023:2023.2007.2013.23292624. Brügge E, Ricchizzi S, Arenbeck M, Keller MN, Schur L, Stummer W, Holling M, Lu MH, Darici D: Large language models improve clinical decision making of medical students through patient simulation and structured feedback: a randomized controlled trial . BMC Med Educ 2024, 24 (1):1391. Yilmaz R, Fazlollahi AM, Winkler-Schwartz A, Wang A, Makhani HH, Alsayegh A, Bakhaidar M, Tran DH, Santaguida C, Del Maestro RF: Effect of Feedback Modality on Simulated Surgical Skills Learning Using Automated Educational Systems- A Four-Arm Randomized Control Trial . J Surg Educ 2024, 81 (2):275–287. Fazlollahi AM, Yilmaz R, Winkler-Schwartz A, Mirchi N, Ledwos N, Bakhaidar M, Alsayegh A, Del Maestro RF: AI in Surgical Curriculum Design and Unintended Outcomes for Technical Competencies in Simulation Training . JAMA Netw Open 2023, 6 (9):e2334658. Turner L, Hashimoto DA, Vasisht S, Schaye V: Demystifying AI: Current State and Future Role in Medical Education Assessment . Acad Med 2024, 99 (4S Suppl 1):S42-s47. Ebina K, Abe T, Hotta K, Higuchi M, Furumido J, Iwahara N, Kon M, Miyaji K, Shibuya S, Lingbo Y et al : Objective evaluation of laparoscopic surgical skills in wet lab training based on motion analysis and machine learning . Langenbecks Arch Surg 2022, 407 (5):2123–2132. Kirubarajan A, Young D, Khan S, Crasto N, Sobel M, Sussman D: Artificial Intelligence and Surgical Education: A Systematic Scoping Review of Interventions . J Surg Educ 2022, 79 (2):500–515. Civaner MM, Uncu Y, Bulut F, Chalil EG, Tatli A: Artificial intelligence in medical education: a cross-sectional needs assessment . BMC Medical Education 2022, 22 (1):772. Lee H: The rise of ChatGPT: Exploring its potential in medical education . Anat Sci Educ 2024, 17 (5):926–931. Alhomoud FK, Basil M, Bondarev AJJoc, JCDR dr: Knowledge, attitudes and practices (KAP) relating to dietary supplements among health sciences and non-health sciences students in one of the universities of United Arab Emirates (UAE) . 2016, 10 (9):JC05. Jaber Amin MH, Mohamed Elhassan Elmahi MA, Abdelmonim GA, Fadlalmoula GA, Jaber Amin JH, Khalid Alrabee NH, Awad MH, Mohamed Omer ZY, Abu Dayyeh NTI, Hassan Abdalkareem NA et al : Knowledge, attitude, and practice of artificial intelligence among medical students in Sudan: a cross-sectional study . Ann Med Surg (Lond) 2024, 86 (7):3917–3923. Ahmer H, Altaf SB, Khan HM, Bhatti IA, Ahmad S, Shahzad S, Naseem S: Knowledge and perception of medical students towards the use of artificial intelligence in healthcare . J Pak Med Assoc 2023, 73 (2):448–451. Al-Qerem W, Eberhardt J, Jarab A, Al Bawab AQ, Hammad A, Alasmari F, Alazab B, Husein DA, Alazab J, Al-Beool S: Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions' students in Jordan . BMC Med Inform Decis Mak 2023, 23 (1):288. Boillat T, Nawaz FA, Rivas H: Readiness to Embrace Artificial Intelligence Among Medical Doctors and Students: Questionnaire-Based Study . JMIR Med Educ 2022, 8 (2):e34973. 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 Medical Education 2024, 24 (1):401. Glover WJ, Li Z, Pachamanova D: The AI-Enhanced Future of Health Care Administrative Task Management . Catalyst non-issue content 2022, 3 (2). Chen M, Zhang B, Cai Z, Seery S, Gonzalez MJ, Ali NM, Ren R, Qiao Y, Xue P, Jiang Y: Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey . Front Med (Lausanne) 2022, 9 :990604. Temsah A, Alhasan K, Altamimi I, Jamal A, Al-Eyadhy A, Malki KH, Temsah M-H: DeepSeek in Healthcare: Revealing Opportunities and Steering Challenges of a New Open-Source Artificial Intelligence Frontier . Cureus 2025, 17 (2). Syed W, Basil AA-RM: Assessment of Awareness, Perceptions, and Opinions towards Artificial Intelligence among Healthcare Students in Riyadh, Saudi Arabia . Medicina (Kaunas) 2023, 59 (5). Busch F, Hoffmann L, Truhn D, Palaian S, Alomar M, Shpati K, Makowski MR, Bressem KK, Adams LC: International pharmacy students' perceptions towards artificial intelligence in medicine-A multinational, multicentre cross-sectional study . Br J Clin Pharmacol 2024, 90 (3):649–661. Alruwaili MM, Abuadas FH, Alsadi M, Alruwaili AN, Elsayed Ramadan OM, Shaban M, Al Thobaity A, Alkahtani SM, El Arab RA: Exploring nurses' awareness and attitudes toward artificial intelligence: Implications for nursing practice . Digit Health 2024, 10 :20552076241271803. Pan G, Ni J: A cross sectional investigation of ChatGPT-like large language models application among medical students in China . BMC Med Educ 2024, 24 (1):908. Jebreen K, Radwan E, Kammoun-Rebai W, Alattar E, Radwan A, Safi W, Radwan W, Alajez M: Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine . BMC Medical Education 2024, 24 (1):507. Sarfaraz S, Khurshid Z, Zafar MS: Use of artificial intelligence in medical education: A strength or an infirmity . J Taibah Univ Med Sci 2023, 18 (6):1553–1554. Jong M: Transforming medical education in the AI era: Balancing technological expertise with humanistic care in tomorrow’s doctors . Ann Acad Med Singap 2024 2024, 53 . Fazlollahi AM, Bakhaidar M, Alsayegh A, Yilmaz R, Winkler-Schwartz A, Mirchi N, Langleben I, Ledwos N, Sabbagh AJ, Bajunaid K et al : Effect of Artificial Intelligence Tutoring vs Expert Instruction on Learning Simulated Surgical Skills Among Medical Students: A Randomized Clinical Trial . JAMA Netw Open 2022, 5 (2):e2149008. Alkaissi H, McFarlane SI: Artificial Hallucinations in ChatGPT: Implications in Scientific Writing . Cureus 2023, 15 (2):e35179. Aljamaan F, Temsah MH, Altamimi I, Al-Eyadhy A, Jamal A, Alhasan K, Mesallam TA, Farahat M, Malki KH: Reference Hallucination Score for Medical Artificial Intelligence Chatbots: Development and Usability Study . JMIR Med Inform 2024, 12 :e54345. Temsah MH, Alhuzaimi AN, Almansour M, Aljamaan F, Alhasan K, Batarfi MA, Altamimi I, Alharbi A, Alsuhaibani AA, Alwakeel L et al : Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases . J Med Syst 2024, 48 (1):54. Stewart J, Lu J, Gahungu N, Goudie A, Fegan PG, Bennamoun M, Sprivulis P, Dwivedi G: Western Australian medical students' attitudes towards artificial intelligence in healthcare . PLoS One 2023, 18 (8):e0290642. Sezgin E: Artificial intelligence in healthcare: Complementing, not replacing, doctors and healthcare providers . Digit Health 2023, 9 :20552076231186520. Zarei M, Eftekhari Mamaghani H, Abbasi A, Hosseini M-S: Application of artificial intelligence in medical education: A review of benefits, challenges, and solutions . Medicina Clínica Práctica 2024, 7 (2):100422. Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Supplementaryfile1.pdf 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-6222830","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457186984,"identity":"fa6ac7a4-9cd1-43a1-aea8-03a72b15655a","order_by":0,"name":"Fadi Aljamaan","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Fadi","middleName":"","lastName":"Aljamaan","suffix":""},{"id":457186985,"identity":"b31d0dba-af8e-4808-b755-caa3ab59f3e9","order_by":1,"name":"Muhammad Faisal Mubarak","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Faisal","lastName":"Mubarak","suffix":""},{"id":457186986,"identity":"bd624eb8-c4e5-4f97-a4cd-c9da362c6381","order_by":2,"name":"Ibraheem Altamimi","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Ibraheem","middleName":"","lastName":"Altamimi","suffix":""},{"id":457186989,"identity":"30fcb779-66db-4582-947b-507375db929f","order_by":3,"name":"Alaa A. Alanteet","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Alaa","middleName":"A.","lastName":"Alanteet","suffix":""},{"id":457186992,"identity":"505b8b01-a5c5-4694-889c-4fd5aede4827","order_by":4,"name":"Mohammed A. Alsalman","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"A.","lastName":"Alsalman","suffix":""},{"id":457186995,"identity":"efa977ae-7d2a-4e6d-9ae1-3b78010ac2a1","order_by":5,"name":"Shereen A. Dasuqi","email":"","orcid":"","institution":"King Khalid University Hospital, King Saud University Medical City","correspondingAuthor":false,"prefix":"","firstName":"Shereen","middleName":"A.","lastName":"Dasuqi","suffix":""},{"id":457186998,"identity":"a7b570b2-63ab-4f73-acf2-50c73fe4a1a6","order_by":6,"name":"Rashid Alballaa","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Rashid","middleName":"","lastName":"Alballaa","suffix":""},{"id":457186999,"identity":"34ec24a9-58fe-4258-a86c-64010aa81166","order_by":7,"name":"Mohammed I Alarifi","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"I","lastName":"Alarifi","suffix":""},{"id":457187000,"identity":"bf43fd1f-0103-4815-82f9-8137680d7b12","order_by":8,"name":"Abdalrhman Al Saadon","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Abdalrhman","middleName":"Al","lastName":"Saadon","suffix":""},{"id":457187001,"identity":"15863895-1c82-414d-aa17-b2319c1588a5","order_by":9,"name":"Abdulrahman O. Alhaqbani","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Abdulrahman","middleName":"O.","lastName":"Alhaqbani","suffix":""},{"id":457187002,"identity":"852879d8-6eea-48c6-a44b-a12d960ff7c9","order_by":10,"name":"Abdulrahman A. Alhadlaq","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Abdulrahman","middleName":"A.","lastName":"Alhadlaq","suffix":""},{"id":457187003,"identity":"00dc5ddc-10c4-452b-94ea-eb8e7b5e6760","order_by":11,"name":"Shirin H. Alokayli","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Shirin","middleName":"H.","lastName":"Alokayli","suffix":""},{"id":457187007,"identity":"c59e0bac-842a-41ce-b6ba-9ec622b01b06","order_by":12,"name":"Bader N. Alrasheed","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Bader","middleName":"N.","lastName":"Alrasheed","suffix":""},{"id":457187008,"identity":"313af232-6456-4a8b-85fb-23204524e897","order_by":13,"name":"Sarah I. Alkhalife","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"I.","lastName":"Alkhalife","suffix":""},{"id":457187009,"identity":"6bbf9529-d5f8-43b3-a718-2f7a749a8557","order_by":14,"name":"Kamran Sattar","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Kamran","middleName":"","lastName":"Sattar","suffix":""},{"id":457187010,"identity":"1496a821-d153-4cd3-b5e0-c15931b2bc8b","order_by":15,"name":"Amr Jamal","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Amr","middleName":"","lastName":"Jamal","suffix":""},{"id":457187011,"identity":"3079ea27-8f33-45fb-bda7-06f2e2025db5","order_by":16,"name":"Mona Soliman","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Mona","middleName":"","lastName":"Soliman","suffix":""},{"id":457187012,"identity":"03f092d3-435a-433f-8668-59b9ed84345e","order_by":17,"name":"Mohamad-Hani Temsah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYPCCA3ISYJqNGMUQRQeMSdeSOINoLbrzm49u/FFzJ31m/xkDhg9lhxnk2w/g12J2jC3tNs+xZ7mzJXIMGGecO8xgcCaBkBYes9sMbIdz50nwGDDztgG1MBDUwv/t5o9/h9Pl+M8YMP8FapHvf0DQFrYbQMMTpBlyDJgZgVoYbhC0Jc3sNm/fYcOZM9IKDvacS+cxuEHIlsOHn9388e2wvMT5wxsf/CizlpPvJ2ALCjgAxDwkqB8Fo2AUjIJRgAsAAKRiR+ctoWs2AAAAAElFTkSuQmCC","orcid":"","institution":"King Saud University","correspondingAuthor":true,"prefix":"","firstName":"Mohamad-Hani","middleName":"","lastName":"Temsah","suffix":""}],"badges":[],"createdAt":"2025-03-13 23:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6222830/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6222830/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82899846,"identity":"b8ea87bd-783d-4d82-a8e7-e3f133947fa9","added_by":"auto","created_at":"2025-05-16 13:21:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17126,"visible":true,"origin":"","legend":"\u003cp\u003eParticipants’ familiarity with AI role in medical education\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6222830/v1/4b623dbff169e3a57b5bcc5d.png"},{"id":82899849,"identity":"4974a437-85ee-49c9-81d0-82e38543c46f","added_by":"auto","created_at":"2025-05-16 13:21:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23012,"visible":true,"origin":"","legend":"\u003cp\u003eParticipants’ perception of AI chatbots future impact on medical education\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6222830/v1/896ead048bdcea076055a609.png"},{"id":82898918,"identity":"345551c3-48c0-424c-a776-53d966958ab2","added_by":"auto","created_at":"2025-05-16 13:13:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17835,"visible":true,"origin":"","legend":"\u003cp\u003eParticipants’ opinion of incorporating AI in teaching methodology of their medical school\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6222830/v1/adc0ea08f62f76281310b658.png"},{"id":82898916,"identity":"1eb46a54-accc-42f7-97e3-1226ab725cb6","added_by":"auto","created_at":"2025-05-16 13:13:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":36468,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eParticipants’ perception of AI implication in medical education compared to traditional teaching methods\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6222830/v1/ed77ac2955f4e2113fa89a63.png"},{"id":82899847,"identity":"3ce4e419-2fe2-4510-932e-fbf66858fc1e","added_by":"auto","created_at":"2025-05-16 13:21:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":29187,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eParticipants' perception of AI Chatbots contribution on medical students’ academic achievement\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6222830/v1/60715e25dfdc04e1e726c91e.png"},{"id":89907336,"identity":"3bf8e9af-0b31-456f-93c1-1b4ef364b353","added_by":"auto","created_at":"2025-08-26 10:17:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4314018,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6222830/v1/4869855c-8715-44fe-8d18-703362e321c0.pdf"},{"id":82898914,"identity":"39a4e725-21c1-46f0-b0bb-f0d1218eda4a","added_by":"auto","created_at":"2025-05-16 13:13:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18299,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-6222830/v1/99923fdaedf7f78d56ab3041.docx"},{"id":82898917,"identity":"d0375231-3d4b-4308-a766-e9de8d8a328d","added_by":"auto","created_at":"2025-05-16 13:13:40","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":37611,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6222830/v1/f92985c428136bd851dc9e63.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Generative Artificial Intelligence Integration in Medical Education: A Cross-Sectional Survey of Medical Students’ Perceptions and Attitudes in Saudi Arabia","fulltext":[{"header":"Practice Points","content":"\u003cul\u003e\n \u003cli\u003eSaudi medical students widely support integrating AI into medical education, particularly in basic sciences.\u003c/li\u003e\n \u003cli\u003eAI chatbots are valued as effective information sources but questioned for reliability and clinical skill training.\u003c/li\u003e\n \u003cli\u003eEthical concerns and fabricated references (hallucinations) are significant barriers to AI adoption.\u003c/li\u003e\n \u003cli\u003eStudents prefer human tutors over AI for clinical education.\u003c/li\u003e\n \u003cli\u003eStructured guidelines are crucial for responsible AI integration.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eGlossary:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eGenerative Artificial Intelligence (Generative AI): A type of artificial intelligence technology that creates original content by learning patterns from large datasets. It can generate text, images, audio, or other media, based on prompts provided by users. Examples include ChatGPT and similar large language models (LLMs).\u003c/li\u003e\n \u003cli\u003eAI Chatbot Hallucination (Fabrication): A phenomenon occurring in generative AI systems, where the chatbot provides seemingly accurate but entirely fabricated or incorrect information, including references or sources. Hallucinations can mislead users into trusting false content, posing challenges for reliable integration in educational settings.\u003c/li\u003e\n \u003cli\u003eAI-driven Assessment: The use of artificial intelligence technologies to evaluate learners' performance objectively, systematically, and without human bias. It involves automated grading, feedback, and competence measurement. While considered fair and efficient, concerns remain regarding transparency, accuracy, and alignment with educational objectives.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eDuring the last century, the landscape of medical care underwent a transformative evolution, from the development of mega size literature databases that spreads easily scholars\u0026rsquo; achievements across the globe, to various technological and digital achievements that boosted medical care and outcomes in the last fifty years [1]. Medical education, which is the foundation source for producing capable healthcare professionals, has been affected by this revolution, too [2]. Educational domains like simulation, clinical skills and patient care training have been the major fields of evolution in that regard.\u003c/p\u003e \u003cp\u003eThe recent COVID-19 pandemic, with its lockdowns and social distancing measures, has significantly accelerated the adoption of telemedicine, virtual training, and online education [3]. The pandemic paved the road for rapid integration and advancements of artificial intelligence (AI) technologies as part of digital health that has been adopted and integrated broadly after the pandemic [4]. OpenAI's Chat Generative Pre-Trained Transformer (ChatGPT) is one of the pioneer advanced large language models (LLMs) that have been launched and stepped multiple evolutionary steps recently [5]. In medical education, this evolution is more than a technological leap; as it signifies a paradigm shift in how medical students and tutors\u0026rsquo; access, interact with, and process medical and non-medical information [6]\u003c/p\u003e \u003cp\u003eNotably, ChatGPT and similar LLMs are opening new dimensions in AI integration into healthcare. LLM technology shows promise in supporting the needs of learners and tutors, assisting in learner assessments, reviewing educational curricula, and ensuring alignment with educational objectives [7, 8]. As source of information, LLM has been shown to surpass conventional information sources like Google's search engine in terms of their output, summarization capability, organization and relevance to search questions [9, 10]. While preliminary studies have underscored ChatGPT\u0026rsquo;s potential in providing medical students with information across various specialties with variable reliability, there remains a critical need to ensure its standardization, credibility, integrity, and ethical uses [11\u0026ndash;13].\u003c/p\u003e \u003cp\u003eUsage of AI chatbots like ChatGPT was associated with criticism in terms of their output\u0026rsquo;s credibility and information sources\u0026rsquo; validity [14]. A study addressing ChatGPT implementation in medical education revealed its valued and efficient role in information gathering and summarization. However, concerns were noted about its content's critical depth, ambiguous sourcing and reliability, highlighting a need for a methodology to critically appraise its output and deal with ChatGPT in a balanced careful strategy [15]. Another valued potential of LLM in medical education is procedural and simulation education, multiple studies that assessed digital feedback on procedural skills training have shown promising learning outcomes [16, 17], while other studies also showed some negative outcomes that directed attention to protocolizing and objecting such computer assessed skills learning in order to improve outcomes and avoid any unintended disadvantages [18]. AI technology has been also implemented in learning objectives assessment with variable success [19\u0026ndash;21].\u003c/p\u003e \u003cp\u003eThe pace of AI development poses an urgent call for academics and medical educators to revisit and revamp their curricula and integrate these technologies in teaching methodology and assessment processes. It is imperative to adopt AI technology in teaching methods and enhance their outcomes, in addition to constructing ethics of conduct for their use among medical students, ensuring they are well-equipped for this rapidly evolving digital era [22, 23]. This paper proposes both immediate and long-term strategies, enabling medical educators to effectively steer through the challenges and opportunities presented in this escalating era of AI-chatbots and evolving LLMs.\u003c/p\u003e \u003cp\u003eThe primary objective of this study is to assess Saudi Arabian undergraduate medical students\u0026rsquo; perception regarding the integration of AI technology within the educational framework in the initial stages of AI applications launch like ChatGPT. We also aimed to investigate medical students\u0026rsquo; insights and perception of AI technology usage in the different domains of undergraduate medical education. The insights gained from this study will contribute to understanding the role of generative AI chatbots in shaping the future of medical education.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy design\u003c/h2\u003e\n \u003cp\u003eCross-sectional survey-based study targeting undergraduate medical students. The questionnaire used in this study was specifically developed by the research team to assess medical students\u0026rsquo; perceptions and attitudes towards generative AI in medical education. An English version of the survey is provided as a supplementary file (see Supplementary File 1).\u003c/p\u003e\n \u003cp\u003eThe survey development process involved a comprehensive literature review and adoption from previous studies that targeted same or similar objectives. The work was followed by four focus group discussions (FGDs), to refine and finalize the survey based on expected domains of Generative Artificial Intelligence integration and employment in medical education, in addition to expected challenges and impact on medical education, patient care and healthcare sector in general. The final survey was reviewed by a multidisciplinary team comprising a pediatric intensivist professor (last author), an adult intensivist (first author), a medical educationist, a biostatistician and a group of undergraduate medical students at different levels (third, fifth and tenth to fourteenth authors). This diverse team of experts empowered the survey\u0026apos;s robustness, by focusing on its content, validity and relevance. The refined survey was then pilot-tested with 30 medical students at different academic levels, ensuring its content validity, clarity and suitability. Feedback from this pilot study led to further refinement, enhancing the survey\u0026apos;s overall structure and reliability.\u003c/p\u003e\n \u003cp\u003eThe survey was structured into three parts: The first part explored medical students\u0026apos; knowledge about generative AI technology and its usage in their educational context, particularly focusing on ChatGPT. Additionally, we explored the students\u0026rsquo; perceptions of evolving AI technology\u0026rsquo;s potential future impact on medical education and their acceptance of adopting these technologies into their curriculum. The second part explored their perceptions and opinions about the role of generative AI in medical education, probing into its potential impact across various domains of medical education, i.e. basic sciences, clinical sciences, simulation, skill-based teaching and academic assessment and evaluation process. The final part gathered information on the participants\u0026apos; demographics and computer literacy. The questionnaire predominantly consisted of multiple-choice questions, encompassing a range of response types, and dichotomous questions (yes/no) with a neutral choice. This design allowed us to capture a comprehensive spectrum of responses. The survey was distributed among a diverse group of medical students, with the intention of covering the wide array of academic levels, within universities that have colleges of medicine in KSA.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eParticipant Recruitment and Sampling Methodology\u003c/h3\u003e\n\u003cp\u003eTo effectively gather data from a diverse range of medical students across Saudi Arabia, the research team adopted a digital-centric approach for survey distribution. The survey was hosted on SurveyMonkey for its proven efficiency in electronic dissemination and export of data for statistical analysis. The study was conducted over a four-week period from October 1 to October 30, 2023. Utilizing social media platforms widely used by the medical student community, such as X and WhatsApp, along with email invitations and personal contacts of the research team, the study aimed to ensure wide geographic coverage and inclusivity. The inclusion criteria consisted of any undergraduate medical students in any medical school within KSA. This method not only facilitated the recruitment of a broad spectrum of participants from various regions of Saudi Arabia but also aligned with the high engagement of medical students on these platforms, meeting the study\u0026apos;s inclusion criteria.\u003c/p\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eThe required sample size was calculated using the Raosoft Sample Size Calculator [24]. The calculated sample size required was 386 medical students assuming estimated proportion of medical students using ChatGPT of 50%, margin of error of 5%, a confidence level of 95%, and a study power of 80%. To accommodate potential incomplete responses and non-responses, this number was increased by 20%, bringing the minimum required sample size to 463 medical students.\u003c/p\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. The study received ethical approval from the Institutional Review Board (IRB) at King Saud University, Riyadh, Saudi Arabia (Approval # E-23-7847). The purpose of the study was clearly outlined on the first page of the electronic survey, where informed consent was also obtained. For any queries, participants were given the option to contact the principal investigator via email. Participation in this study was entirely voluntary, and no financial or other incentives were provided to participants. Participants were informed that they could withdraw from the study at any time without facing any penalty or consequence. To maintain confidentiality, no personal identifiers were collected from the respondents.\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eMean and standard deviations were used to describe continuous measured variables, frequencies and percentages for the categorically measured variables. The Kolmogrove-Smirnove (K-S) statistical test of normality and the histograms were used to assess the statistical normality assumption for metric variables. The Kuder-Richardson\u0026apos;s test of internal consistency was used to assess the reliability of the binary measured questionnaire which was 0.71 for 16 tested dichotomous questions. The multivariable Logistic Binary Regression Analysis was applied to assess the statistical significance of participants\u0026rsquo; measured binary dichotomized perceptions. The association between the predictor independent variables with the analyzed outcomes in the logistic regression analysis was expressed as Odds Ratios with their associated 95% confidence intervals. SPSS IBM statistical software version 28 was used for statistical data analysis. The alpha significance level was considered at 0.050 level.\u003c/p\u003e\n\u003c/div\u003e\n"},{"header":"Results","content":"\n\u003cp\u003eA total of 1039 medical students participated in our study with predominant male representation 64.3%. The participants split between junior medical students in basic science or non-clinical years 40.7% and senior students 47.3% in clinical years, while medical students in their internship year were 12%. The vast majority expressed very good to excellent computer skills 76.1%. The participants were 57.2% familiar with AI chatbots compared to 42.8% who were not. The vast majority admitted being able to access AI chatbots 75.5% (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive Analysis of medical students sociodemographic and academic characteristics (N\u0026thinsp;=\u0026thinsp;1039).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJunior students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSenior students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipants\u0026rsquo; computer skills\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFair\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExcellent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamiliarity with AI Chatbots role in medical education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccessibility to AI Chatbots\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, 57.3% were familiar with AI role in medical education, while 86.4% of them believed AI will have future impact on medical education future Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, and 70.1% agreed with incorporating AI technology in their medical school curriculum Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eParticipants\u0026rsquo; perception of AI technology impact in medical education is shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, where 73.4% believed AI chatbots will have an impact on the basic sciences portion of medical education, followed by simulation-based teaching 58.1%, clinical sciences 54.9%, and only 41.6% with an impact on clinical skills teaching (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eParticipants\u0026rsquo; perception of AI implementation in medical education\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhich of the following is a potential impact of AI in medical education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinical sciences (e.g. Internal medicine, OB/GYN)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasic sciences (e.g. Physiology, Anatomy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinical Skills (e.g. Physical examination, surgical techniques)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther (Assignments and Homework)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eDo you think AI as a tutor would be more efficient and informative than a human tutor for basic science teaching?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eDo you think AI as a tutor would be more efficient and informative than a human tutor for clinical teaching, especially skills?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eDo you think AI based assessment versus human tutor would be fairer and more objective?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eDo you think medical students\u0026rsquo; usage of AI Chatbots in assignments completion is ethical?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eAre AI Chatbots reliable sources of information for medical students?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eAre you aware of references fabrication/hallucination encountered with AI Chatbots?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eIs AI Chatbots\u0026rsquo; references confusion/hallucination an obstacle for its use in medical education?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eOnly 29.8% of participants believed AI technology will be superior to current traditional teaching methodology in medical schools, while 21.1% believed the opposite, and the rest were undetermined as shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The vast majority (80%) did not think \u0026lsquo;AI-harnessed medical teaching technology\u0026rsquo; would be more informative than \u0026lsquo;traditional human tutor\u0026rsquo; for clinical sciences and 62.4% did not think so for basic sciences as shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. On the other hand, 51.7% believed that \u0026lsquo;AI assisted academic assessment\u0026rsquo; would be fairer and more objective compared to \u0026lsquo;human tutoring.\u0026rsquo; The participants split equally regarding their perception of ethicality using AI chatbots in their assignments. Whereas 60.7% of the participants believed that AI technology will improve their academic achievements, while 9.3% believed there was a lack of effect in this, and the rest were undetermined (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eOnly 47.4% believed AI chatbots as reliable sources of information for medical students. In the same context, 40.4% were aware of AI chatbots references\u0026rsquo; fabrication/hallucination with 64% of participants perceiving this fallacy to pose an obstacle of incorporating them into medical education (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWe also assessed participants\u0026rsquo; perception of AI impact on the future of medical practice as shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and we found 71.1% believed that physicians\u0026rsquo; access to AI will affect their future competencies and efficiencies. While 44.7% believed it might lead to the replacement of some medical specialties in the future, 33.8% did not believe so, and 21.6% were undetermined in that regard. On the other hand, 61.1% believed AI technology will improve medical practice and patient care outcomes.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eParticipants\u0026rsquo; perception about AI and future of medical practice\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDo you think physician access to AI Chatbots in the future would affect their competencies and efficiency?\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDo you think AI technology will improve medical practice?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDo you think AI technology will improve patients\u0026rsquo; care?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDo you think AI technology might replace some of the medical specialties in the future?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows multivariate binary logistic regression analysis of the participants\u0026rsquo; characteristics that are associated with their perception of positive impact of AI technology on the future of medical education. Males were significantly less inclined to believe in the positive impact of AI technology on medical education future compared to females (39% times less, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.028). Medical students who agreed with superiority of AI compared to traditional tutors in terms of clinical teaching efficiency and informativity were significantly less inclined to believe in its future positive impact on medical education (50.4% times less, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.006). However, students who encouraged the incorporation of AI technology in medical schools teaching methods, and those who believed AI chatbots are reliable source of medical information, were significantly more inclined to believe in their positive impact on medical education future (3.83 times more, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, 1.926 times more, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003) respectively. On the other hand, students who believed AI chatbots can improve medical students\u0026rsquo; academic achievement were significantly less inclined to believe in AI technology positive impact on the future of medical education (23.5% times less, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.020). Similarly, those who believed AI technology will improve medical practice or patients\u0026rsquo; care were significantly less optimistic of its positive impact on the medical education future (40.3% times less, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, 23.5% times less, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.027), respectively.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariable Logistic Binary Regression Analysis of participants\u0026rsquo; belief in the impact of AI technology on medical education future\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMultivariate adjusted odds Ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOR 95% C.I.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI more efficient and informative for clinical teaching\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEncourages AI incorporation in medical teaching methodology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI Chatbots are reliable source of information for medical student\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI would improve medical students\u0026rsquo; academic achievement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI will improve medical practice\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI will improve patients\u0026rsquo; care\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eDV: Participants\u0026rsquo; belief in the impact of AI technology on medical education future\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the multivariable binary logistic regression analysis for the odds of participants\u0026rsquo; characteristics associated with their agreement to incorporate AI technology in medical education methodology. Male medical students compared to females were significantly more disagreeing with incorporating AI technology in medical education methodology (32.4% times less agreeing, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022). While senior medical students (22 years or older) compared to others were significantly more agreeing with incorporating AI technology in medical education methodology (1.57 times more, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e\n\u003cp\u003eMedical students who had access to AI chatbots were significantly more agreeing to incorporate AI technology in medical schools teaching methodology (59.5% \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). Medical students who believed in AI chatbots\u0026rsquo; usefulness in medical simulation-based teaching, and its superiority to human tutor for teaching basic science, were significantly more in agreement in incorporating them into medical schools teaching methodology (1.439 times more, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015, 2.117 times more, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) respectively. Medical students who believed that AI-assisted academic assessment would be fairer and more objective than human evaluators were significantly more agreeing in incorporating AI technology in medical schools teaching methodology (1.667 times more, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001)\u003c/p\u003e\n\u003cp\u003eThe medical students who believed that using AI chatbots in assignment completion is ethical were significantly more in agreement in incorporating this technology in teaching methodology (1.593 times more, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e\n\u003cp\u003eOn the other side, those who believed that AI chatbots usage would enhance their academic achievement were significantly less agreeing to incorporate AI technology in education methodology (28.3% times less, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Medical students who perceived AI technology will improve patients\u0026rsquo; outcomes were significantly less agreeing to incorporate it in their medical schools teaching methodology (24.4% times less, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). \u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariable Logistic Binary Regression Analysis of participants\u0026rsquo; agreement with AI incorporation in medical school education methodology\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMultivariate adjusted Odds Ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOR 95% C.I.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUniversity location\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI Chatbots ease of access\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI has a potential impact in simulation-based teaching\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI more efficient and informative for basic sciences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI based academic assessment would be fairer and more objective\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical students\u0026rsquo; usage of AI Chatbots in assignments completion is ethical\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI would improve medical students\u0026rsquo; academic achievement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI will improve patients\u0026rsquo; care\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eDV: participants\u0026rsquo; agreement with AI incorporation in medical school education methodology\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e sheds light on the participating medical students\u0026rsquo; characteristics associated with their belief in AI chatbots\u0026rsquo; reliability as medical information source. Male medical students compared to females had significantly lower belief (31.6% times less, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). Those who had strong belief in AI role in simulation teaching had significantly less belief in its information source reliability (24.1% times less, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048). While those who believed AI would be more efficient than human tutors in basic science, clinical skills teaching and is potentially a fairer evaluator, had significantly higher trust in AI medical information reliability (66.5%, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001, 82.4%, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002, 36.2%, \u003cem\u003ep-value\u003c/em\u003e .029) respectively. Medical students who believed using AI chatbots in assignment completion is ethical had significantly higher belief in their medical information reliability (63.2%, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0010). On the other hand, those who expected AI will have positive impact on medical education future had significantly less belief in AI chatbots medical information reliability (21.4% times less, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003). Medical students\u0026rsquo; belief that AI might have an impact on the future of medical education correlated significantly and positively with their belief in its information reliability (223%, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), but those who believed this impact is due to its superiority compared to traditional medical education methods had less trust in its medical information reliability (20% times less, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.006). However, medical students\u0026rsquo; belief that AI chatbots\u0026rsquo; hallucination would be an obstacle for its incorporation in medical education had less belief in its medical information reliability (33.2% times less, \u003cem\u003ep-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.004)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariable Logistic Binary Regression Analysis of medical students\u0026rsquo; odds believing in the reliability of AI chatbots as source of medical information\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMultivariate adjusted Odds Ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOR 95% C.I.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge(years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI usefulness in medical simulation (Clinical scenarios)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI would be more efficient than human tutor for basic science teaching\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI would be more efficient than a human tutor for clinical skills teaching\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI based academic assessment would be fairer and more objective\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical students\u0026rsquo; usage of AI Chatbots in assignments completion is ethical\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI Chatbots would impact medical education future\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI Chatbots references hallucination is an obstacle for its application in medical education\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI will improve medical practice\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv align=\"char\" class=\"colspec\"\u003eDV: Participants\u0026rsquo; belief in AI chatbots as reliable source for medical education\u003c/div\u003e\n\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study is empowered by including large number of medical students 1039. Most of them (75.5%) admitted to being able to access AI chatbots, yet they were less familiar (57%) with areas where it could be applied in medical education. Our decent number of participants’ familiarity with AI chatbots correlates with similar literature findings ranging from 50–85% [25–28]. This low curiosity and interest at the current stage of incorporating Generative AI technology in medical education, points to medical students’ hesitancy and reluctance toward AI role in medical education so far. At the same time, the majority (86.4%) of participants believed of AI inevitable future impact on medical education as supported by similar surveys [29]. In comparison to our studied cohort, Jordanian medical students, who are very close geographically, sociologically, and technologically to our population, had lower expectation (67%) of AI’s potential to revolutionize medical education [27].\u003c/p\u003e \u003cp\u003eParticipants high expectations of AI’s future impact on medical education and encouraging its incorporation in medical education, this might be driven by their expectation of its multidimensional tutorship efficiency including basic sciences, simulation teaching to some extent and their potential role to improve students’ academic achievement, that mirrors similar survey studies regarding medical students’ optimism in terms of AI technology application in medical education and assignment preparation [27]. At the same time, the participants had an inconsistent vision regarding AI technology incorporation in medical education, especially when they were questioned if it will have any long-lasting future impact when compared to traditional teaching methodology. Those who believed AI application in medical education would improve clinical practice and patient care outcomes, did not support incorporating AI technology in medical education, this contradiction might be explained by their perception of AI technology role more befitting for administrative, logistical, and organizational roles rather than educational applications [30], or because they perceived academics and students’ skills are still lacking and precluding AI application into medical education as observed similarly in a previous survey [28]. While we observed that participants who had strong belief in AI technology’s positive impact on basic sciences, simulation teaching, and as an academic assessment tool, they had strong expectations and acceptance of incorporating AI technology in medical education. Interestingly, survey takers who believed AI technology would improve their academic achievements, were not supportive of incorporating it into medical education methodology and did not believe it would have an impact in that regard. Thus, limiting their expectations of AI technology to being a source of information, assistive and organizing tool rather than promising for a major revolutionary role in medical education. These positive attitudes in general but with different reservations mirror findings from multiple studies as shown in systematic review by Chen et al, in 2022 of cross-sectional surveys targeting physicians and medical students regarding their acceptance of clinical applications of AI [31].\u003c/p\u003e \u003cp\u003eNoteworthy, 71.1% of participants believed that physicians’ access to AI chatbots will affect their future clinical and career competency, but did not expect positive impact of AI technology on patients’ care and medical practice, that is explained in similar surveys that showed physicians and medical students reservation about clinical artificial intelligence impact on their competencies due to the complexity of the medicine field that requires human physician approach not yet achieved by clinical AI [28]. Also, they did not expect AI technology might replace some medical specialties in the future, Chen systematic review has shown similar findings, there was consensus about the need for collaboration between clinical AI and human physician, in spite of mixed opinions about its future as a surrogate physician [31]. Therefore, medical students and physicians are encouraged to build up and optimize their AI technology competency to keep up with future healthcare systems that will inevitably incorporate clinical Generative AI technology in different sectors [32].\u003c/p\u003e \u003cp\u003eFemale students compared to male students had stronger belief of AI technology impact on the future of medical education, encouraged incorporating it in medical education curriculum, and trusted AI chatbots to be a reliable source of medical information. The literature showed inconsistent findings regarding sex differences in terms of medical and allied healthcare professional students’ optimism for AI future and its impact on medical education [33–37].\u003c/p\u003e \u003cp\u003e70.1% of the students surveyed agreed with incorporating AI technology in their medical school curriculum. However, when questioned about AI technology’s possible superiority compared to traditional teaching methodology, only 29.8% expected it to be superior and 21.1% expected AI technology to be inferior in this regard, but the majority were undetermined. At the same time, the medical students showed inconsistent or reluctant vision for AI impact of different medical education domains especially when compared to traditional human tutors, as they were optimistic of its role for basic sciences education, because they believed AI chatbots to be good sources of information and supported its superiority and had strong expectations of its impact on medical education. While to a lesser extent, participants had less enthusiasm for AI tutor role in comparison to human tutor for simulation and clinical medicine teaching. This doubtful optimism is probably explained by the students’ perception of AI’s role at the current stage as an information source, that summarized and organized the educational material well and presented it in a concise format, rather than a tutoring or mentoring tool and highlights their appreciation of human tutor role in medical education process and their unique skills that cannot be replaced so far by artificial intelligence technology. The effectiveness of human tutorship versus AI tutorship is an ongoing debate in literature with most literature emphasizing the pivotal role of human expert tutorship as being more effective in implanting professionalism and critical thinking skills in medical students. This subset of survey takers perceived certain skills including critical appraisal of existing clinical evidence to be of inherent humanistic skills exclusively [38, 39], Current literature supports concerns over AI application and medical education in relationship to advertent or inadvertent overreliance on AI applications by medical students, evanescence of critical thinking skills and the reliability of LLM output [14]. For the sake of fairness, AI has been accepted widely for its tutoring skills, especially in the field of surgical skills teaching [40].\u003c/p\u003e \u003cp\u003eOne of the possible challenges of incorporating AI chatbots into medical education is lack of information reliability. Only 47.4% of our participants believed AI chatbots will act as a reliable source of information and predicted a high impact on medical education in the future but perceived it to be lacking in the concurrent time. Another challenge is the widely identified phenomenon in the medical literature of information and references’ fabrication/hallucination encountered while utilizing AI applications [41–43], as 64% of survey takers believed it might be an obstacle for AI chatbots incorporation in medical education. Those who were concerned about AI chatbots hallucination had negative expectations regarding their effect on physicians’ future competencies and perceived these hallucinations to be an obstacle to AI incorporation in medical education. Interestingly, these concerns were not associated with significant concerns of AI’s future impact on clinical practice and patient care.\u003c/p\u003e \u003cp\u003eSenior medical students (medical students in their 3rd to 5th years of medical school) had lower expectations that AI technology would affect physicians’ future competencies and efficiency in comparison to basic science students (medical students in their 1st and 2nd years of medical school), this might be related to senior medical students advanced level especially given their exposure to clinical medicine practice. This notion underscores the human factor in favoring AI technology or machine-driven competency in similar observation reported in literature linking students’ knowledge base level and degree of optimism of AI applications in Medicine [36]. 44.7% of our participants expected AI technology to replace some medical specialties in the future albeit not carrying the perception of clinical AI having an adversary effect on their future clinical competency or proficiency. This observation might be explained by perceiving AI technology to be inferior to human competency due to human’s cumulative training, acquired clinical critical thinking skills, and humanistic approach to patients. Our findings streamline reviewed contextual literature that AI will only act to complement and reinforce human physician skills rather than replace them [44, 45].\u003c/p\u003e \u003cp\u003eWhile regarding fairness and objectivity of assessment and evaluation process, participants split equally in their expectation of AI technology in comparison to human tutoring. Students who perceived AI to be fairer and more objective in academic assessment compared to human tutors strongly encouraged its incorporation in medical education methodology and had higher trust in it as source of medical information. Furthermore, the same group of students had high expectations of its impact on medical education and healthcare practice. This trust in AI technology assessment skills has been observed in the literature and credited as an advantageous characteristics for its implementation in education field [46]. The same group of students also had strong belief in AI chatbots’ effect on physicians’ future competency and clinical efficiency. This extreme observation might stem from biased unrealistic expectations of applied AI role in medical education and medical practice without substantive basis. This point is made as previous studies using AI in neurosurgical training showed encouraging results with AI tutoring via feedback in terms of positive emotions [40]. Medical students may perceive AI-administered evaluations free from the personal biases that human evaluators might encounter [46]. However, similarly, AI technology might introduce another element of bias, which is dependent on algorithms and protocols programmed to feed AI platforms during prompting and evaluation processes. Therefore, AI implementation in medical education assessment should be protocolized objectively, skeptically and employed carefully to achieve totalitarian unbiased assessment process.\u003c/p\u003e \u003cp\u003eStudents who believed in AI’s role in simulation and basic science teaching in comparison to clinical teaching, had strong inclination for incorporating AI in medical education and expected positive impact on the medical education future. Participants who believed in AI’s superiority in tutoring clinical and basic sciences teaching had a strong belief in AI chatbots’ information reliability compared to those who encouraged its implementation in simulation teaching who believed more in AI technology assistive and demonstrating role. These findings show a dichotomous perception among survey takers with a group believing strongly in AI application for information resourcing and being supportive of AI adoption in education methodology while the other group perceived AI usefulness in the virtual and robotic applications of AI as information resourcing and diagnostic tools.\u003c/p\u003e \u003cp\u003eInterestingly, the participating students who believed in AI’s potential impact on medical education held a negative perception of AI’s ability to improve patient care and clinical outcomes. This viewpoint seems contradictory to the overall optimism expressed by 60% of the participants, who felt that AI technology would positively affect patient care and medical practice. A plausible explanation to this contradiction could be explained by participants perception of different AI role in medical education in comparison to medical practices and its outcomes, and the participants’ immature understanding of the clinical care process and healthcare systems as various AI applications in the medical field have proven to improve overall patient care especially when it comes to medical decision making in complex cases, inventing personalized therapies for patient with frequent hospital admissions, aiding surgeons during operation, and fine-tuning infection control within a healthcare system [9].\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e:\u003c/h2\u003e \u003c/div\u003e"},{"header":"Limitations","content":"\u003cp\u003eOur survey inherently has potential limitations. It was launched early when generative AI applications had just started their adoption in medical practice. The participants’ humble familiarity with AI applications may have limited their insights about the future of AI technology in medical education and healthcare sectors, though it did give early cross-sectional input on their early experience and anticipations. Our survey did not dig deep into the medical students’ understanding of deep learning methodology of AI technology and the evolution of LLM which would have extended the survey and made it cumbersome to complete. We addressed medical students’ prospects regarding AI technology and medical education and did not assess their tutors.\u003c/p\u003e\u003cp\u003eOur survey was conducted early in the adoption of generative AI in medical education, which may have limited participants' insights into its long-term impact. Their modest familiarity with AI applications at the time could have influenced their perceptions. Additionally, we did not explore students' understanding of deep learning or LLM evolution, nor did we assess educators' perspectives, which are crucial for AI integration. Future research should investigate faculty viewpoints, assess AI’s long-term impact on medical training, and explore the effectiveness of advanced AI models like GPT-o1 or DeepSeek and other domain-specific LLMs in clinical education and decision-making [32].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMedical students expressed strong encouragement of incorporating AI technology into their medical school curricula, with a strong belief in AI capability for enhancing their academic achievements. AI applications have promising roles such as medical information resources, enriching simulation teaching like surgical skills and optimizing basic sciences teaching in anatomy and physiology for example. Medical students demonstrated awareness of human tutor superiority over current AI applications and expressed awareness of serious concerns in relation to potential misuse and overreliance on AI applications. However, a minority of them expressed unrealistic overzealous expectations with the technology that warrants attention by academics soon. In our study, most medical students utilized AI applications as information sources, especially that many AI applications showed superiority in summarizing and organizing medical literature. Other serious concerns were expressed by medical students regarding AI chatbots used in medical literature search protocols resulting in fabrication of information and references. There is an urgent and ongoing need in addressing the accuracy of AI applications\u0026rsquo; outputs in the medical field, to maturate and optimize their implementation in the medical education process.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics Approval:\u003c/h2\u003e \u003cp\u003e Institutional Review Board (IRB) at King Saud University, Riyadh, Saudi Arabia (Approval # E-23-7847)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eClinical trial number\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNone.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDeclaration of Interest\u003c/b\u003e: The authors declare no conflicts of interest related to this study.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData Availability Statement\u003c/b\u003e: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions Statement: F.A., M.F.M., I.A., M.S., and M.-H.T. conceptualized the study and drafted the Methods and Results sections. A.A.A., M.A.A., and S.A.D. contributed to drafting the Discussion section. R.A., M.I.A., A.A.S., A.O.A., A.A.A., S.H.A., B.N.A., and S.I.A. were responsible for drafting the Introduction, preparing the IRB proposal, and conducting data collection. K.S.B. and A.J. critically revised the manuscript, integrating insights on medical education themes and medical informatics. All authors contributed to data interpretation, manuscript revision, and approved the final version for submission. Additionally, all authors agree to be accountable for all aspects of the work, ensuring accuracy and integrity.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e The authors thank the participating medical students and King Saud University for supporting this research. The authors acknowledge the use of ChatGPT-4o to refine the language and enhance the clarity of this manuscript. However, the final text was thoroughly reviewed and edited by the authors, who take full responsibility for its content and accuracy.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData Availability Statement: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Singh S, Bhatt P, Sharma SK, Rabiu S: \u003cb\u003eDigital transformation in healthcare: Innovation and technologies\u003c/b\u003e. In: \u003cem\u003eBlockchain for Healthcare Systems.\u003c/em\u003e edn.: CRC Press; 2021: 61\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Jamal A, Solaiman M, Alhasan K, Temsah M-H, Sayed G: \u003cb\u003eIntegrating ChatGPT in Medical Education: Adapting Curricula to Cultivate Competent Physicians for the AI Era\u003c/b\u003e. \u003cem\u003eCureus\u003c/em\u003e 2023, \u003cb\u003e15\u003c/b\u003e(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Chiesa V, Antony G, Wismar M, Rechel B: \u003cb\u003eCOVID-19 pandemic: health impact of staying at home, social distancing and \u0026lsquo;lockdown\u0026rsquo;measures\u0026mdash;a systematic review of systematic reviews\u003c/b\u003e. \u003cem\u003eJournal of public health\u003c/em\u003e 2021, \u003cb\u003e43\u003c/b\u003e(3):e462-e481.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Metcalf CJE, Lessler J: \u003cb\u003eOpportunities and challenges in modeling emerging infectious diseases\u003c/b\u003e. \u003cem\u003eScience (New York, NY)\u003c/em\u003e 2017, \u003cb\u003e357\u003c/b\u003e(6347):149\u0026ndash;152.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Shoja MM, Van de Ridder JMM, Rajput V: \u003cb\u003eThe Emerging Role of Generative Artificial Intelligence in Medical Education, Research, and Practice\u003c/b\u003e. \u003cem\u003eCureus\u003c/em\u003e 2023, \u003cb\u003e15\u003c/b\u003e(6):e40883.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Stoumpos AI, Kitsios F, Talias MA: \u003cb\u003eDigital Transformation in Healthcare: Technology Acceptance and Its Applications\u003c/b\u003e. \u003cem\u003eInt J Environ Res Public Health\u003c/em\u003e 2023, \u003cb\u003e20\u003c/b\u003e(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Chan KS, Zary N: \u003cb\u003eApplications and Challenges of Implementing Artificial Intelligence in Medical Education: Integrative Review\u003c/b\u003e. \u003cem\u003eJMIR Med Educ\u003c/em\u003e 2019, \u003cb\u003e5\u003c/b\u003e(1):e13930.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Khamis N, Chen B, Egan C, Gaglani S, Tackett S: \u003cb\u003eMore intelligent faculty development: Integrating GenAI in curriculum development programs\u003c/b\u003e. \u003cem\u003eMed Teach\u003c/em\u003e 2025:1\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Haleem A, Javaid M, Khan IH: \u003cb\u003eCurrent status and applications of Artificial Intelligence (AI) in medical field: An overview\u003c/b\u003e. \u003cem\u003eCurrent Medicine Research and Practice\u003c/em\u003e 2019, \u003cb\u003e9\u003c/b\u003e(6):231\u0026ndash;237.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Breeding T, Martinez B, Patel H, Nasef H, Arif H, Nakayama D, Elkbuli A: \u003cb\u003eThe Utilization of ChatGPT in Reshaping Future Medical Education and Learning Perspectives: A Curse or a Blessing?\u003c/b\u003e \u003cem\u003eAm Surg\u003c/em\u003e 2024, \u003cb\u003e90\u003c/b\u003e(4):560\u0026ndash;566.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Temsah O, Khan SA, Chaiah Y, Senjab A, Alhasan K, Jamal A, Aljamaan F, Malki KH, Halwani R, Al-Tawfiq JA \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eOverview of Early ChatGPT's Presence in Medical Literature: Insights From a Hybrid Literature Review by ChatGPT and Human Experts\u003c/b\u003e. \u003cem\u003eCureus\u003c/em\u003e 2023, \u003cb\u003e15\u003c/b\u003e(4):e37281.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Fatima A, Shafique MA, Alam K, Fadlalla Ahmed TK, Mustafa MS: \u003cb\u003eChatGPT in medicine: A cross-disciplinary systematic review of ChatGPT's (artificial intelligence) role in research, clinical practice, education, and patient interaction\u003c/b\u003e. \u003cem\u003eMedicine (Baltimore)\u003c/em\u003e 2024, \u003cb\u003e103\u003c/b\u003e(32):e39250.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Abouammoh N, Alhasan K, Aljamaan F, Raina R, Malki KH, Altamimi I, Muaygil R, Wahabi H, Jamal A, Alhaboob A \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003ePerceptions and Earliest Experiences of Medical Students and Faculty With ChatGPT in Medical Education: Qualitative Study\u003c/b\u003e. \u003cem\u003eJMIR Med Educ\u003c/em\u003e 2025, \u003cb\u003e11\u003c/b\u003e:e63400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Ben\u0026iacute;tez TM, Xu Y, Boudreau JD, Kow AWC, Bello F, Van Phuoc L, Wang X, Sun X, Leung GK, Lan Y \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eHarnessing the potential of large language models in medical education: promise and pitfalls\u003c/b\u003e. \u003cem\u003eJ Am Med Inform Assoc\u003c/em\u003e 2024, \u003cb\u003e31\u003c/b\u003e(3):776\u0026ndash;783.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Abouammoh N, Alhasan K, Raina R, Malki KA, Aljamaan F, Tamimi I, Muaygil R, Wahabi H, Jamal A, Al-Tawfiq JA \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eExploring Perceptions and Experiences of ChatGPT in Medical Education: A Qualitative Study Among Medical College Faculty and Students in Saudi Arabia\u003c/b\u003e. \u003cem\u003emedRxiv\u003c/em\u003e 2023:2023.2007.2013.23292624.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Br\u0026uuml;gge E, Ricchizzi S, Arenbeck M, Keller MN, Schur L, Stummer W, Holling M, Lu MH, Darici D: \u003cb\u003eLarge language models improve clinical decision making of medical students through patient simulation and structured feedback: a randomized controlled trial\u003c/b\u003e. \u003cem\u003eBMC Med Educ\u003c/em\u003e 2024, \u003cb\u003e24\u003c/b\u003e(1):1391.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Yilmaz R, Fazlollahi AM, Winkler-Schwartz A, Wang A, Makhani HH, Alsayegh A, Bakhaidar M, Tran DH, Santaguida C, Del Maestro RF: \u003cb\u003eEffect of Feedback Modality on Simulated Surgical Skills Learning Using Automated Educational Systems- A Four-Arm Randomized Control Trial\u003c/b\u003e. \u003cem\u003eJ Surg Educ\u003c/em\u003e 2024, \u003cb\u003e81\u003c/b\u003e(2):275\u0026ndash;287.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Fazlollahi AM, Yilmaz R, Winkler-Schwartz A, Mirchi N, Ledwos N, Bakhaidar M, Alsayegh A, Del Maestro RF: \u003cb\u003eAI in Surgical Curriculum Design and Unintended Outcomes for Technical Competencies in Simulation Training\u003c/b\u003e. \u003cem\u003eJAMA Netw Open\u003c/em\u003e 2023, \u003cb\u003e6\u003c/b\u003e(9):e2334658.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Turner L, Hashimoto DA, Vasisht S, Schaye V: \u003cb\u003eDemystifying AI: Current State and Future Role in Medical Education Assessment\u003c/b\u003e. \u003cem\u003eAcad Med\u003c/em\u003e 2024, \u003cb\u003e99\u003c/b\u003e(4S Suppl 1):S42-s47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Ebina K, Abe T, Hotta K, Higuchi M, Furumido J, Iwahara N, Kon M, Miyaji K, Shibuya S, Lingbo Y \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eObjective evaluation of laparoscopic surgical skills in wet lab training based on motion analysis and machine learning\u003c/b\u003e. \u003cem\u003eLangenbecks Arch Surg\u003c/em\u003e 2022, \u003cb\u003e407\u003c/b\u003e(5):2123\u0026ndash;2132.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Kirubarajan A, Young D, Khan S, Crasto N, Sobel M, Sussman D: \u003cb\u003eArtificial Intelligence and Surgical Education: A Systematic Scoping Review of Interventions\u003c/b\u003e. \u003cem\u003eJ Surg Educ\u003c/em\u003e 2022, \u003cb\u003e79\u003c/b\u003e(2):500\u0026ndash;515.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Civaner MM, Uncu Y, Bulut F, Chalil EG, Tatli A: \u003cb\u003eArtificial intelligence in medical education: a cross-sectional needs assessment\u003c/b\u003e. \u003cem\u003eBMC Medical Education\u003c/em\u003e 2022, \u003cb\u003e22\u003c/b\u003e(1):772.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Lee H: \u003cb\u003eThe rise of ChatGPT: Exploring its potential in medical education\u003c/b\u003e. \u003cem\u003eAnat Sci Educ\u003c/em\u003e 2024, \u003cb\u003e17\u003c/b\u003e(5):926\u0026ndash;931.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Alhomoud FK, Basil M, Bondarev AJJoc, JCDR dr: \u003cb\u003eKnowledge, attitudes and practices (KAP) relating to dietary supplements among health sciences and non-health sciences students in one of the universities of United Arab Emirates (UAE)\u003c/b\u003e. 2016, \u003cb\u003e10\u003c/b\u003e(9):JC05.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Jaber Amin MH, Mohamed Elhassan Elmahi MA, Abdelmonim GA, Fadlalmoula GA, Jaber Amin JH, Khalid Alrabee NH, Awad MH, Mohamed Omer ZY, Abu Dayyeh NTI, Hassan Abdalkareem NA \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eKnowledge, attitude, and practice of artificial intelligence among medical students in Sudan: a cross-sectional study\u003c/b\u003e. \u003cem\u003eAnn Med Surg (Lond)\u003c/em\u003e 2024, \u003cb\u003e86\u003c/b\u003e(7):3917\u0026ndash;3923.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Ahmer H, Altaf SB, Khan HM, Bhatti IA, Ahmad S, Shahzad S, Naseem S: \u003cb\u003eKnowledge and perception of medical students towards the use of artificial intelligence in healthcare\u003c/b\u003e. \u003cem\u003eJ Pak Med Assoc\u003c/em\u003e 2023, \u003cb\u003e73\u003c/b\u003e(2):448\u0026ndash;451.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Al-Qerem W, Eberhardt J, Jarab A, Al Bawab AQ, Hammad A, Alasmari F, Alazab B, Husein DA, Alazab J, Al-Beool S: \u003cb\u003eExploring knowledge, attitudes, and practices towards artificial intelligence among health professions' students in Jordan\u003c/b\u003e. \u003cem\u003eBMC Med Inform Decis Mak\u003c/em\u003e 2023, \u003cb\u003e23\u003c/b\u003e(1):288.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Boillat T, Nawaz FA, Rivas H: \u003cb\u003eReadiness to Embrace Artificial Intelligence Among Medical Doctors and Students: Questionnaire-Based Study\u003c/b\u003e. \u003cem\u003eJMIR Med Educ\u003c/em\u003e 2022, \u003cb\u003e8\u003c/b\u003e(2):e34973.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Laupichler MC, Aster A, Meyerheim M, Raupach T, Mergen M: \u003cb\u003eMedical students\u0026rsquo; AI literacy and attitudes towards AI: a cross-sectional two-center study using pre-validated assessment instruments\u003c/b\u003e. \u003cem\u003eBMC Medical Education\u003c/em\u003e 2024, \u003cb\u003e24\u003c/b\u003e(1):401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Glover WJ, Li Z, Pachamanova D: \u003cb\u003eThe AI-Enhanced Future of Health Care Administrative Task Management\u003c/b\u003e. \u003cem\u003eCatalyst non-issue content\u003c/em\u003e 2022, \u003cb\u003e3\u003c/b\u003e(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Chen M, Zhang B, Cai Z, Seery S, Gonzalez MJ, Ali NM, Ren R, Qiao Y, Xue P, Jiang Y: \u003cb\u003eAcceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey\u003c/b\u003e. \u003cem\u003eFront Med (Lausanne)\u003c/em\u003e 2022, \u003cb\u003e9\u003c/b\u003e:990604.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Temsah A, Alhasan K, Altamimi I, Jamal A, Al-Eyadhy A, Malki KH, Temsah M-H: \u003cb\u003eDeepSeek in Healthcare: Revealing Opportunities and Steering Challenges of a New Open-Source Artificial Intelligence Frontier\u003c/b\u003e. \u003cem\u003eCureus\u003c/em\u003e 2025, \u003cb\u003e17\u003c/b\u003e(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Syed W, Basil AA-RM: \u003cb\u003eAssessment of Awareness, Perceptions, and Opinions towards Artificial Intelligence among Healthcare Students in Riyadh, Saudi Arabia\u003c/b\u003e. \u003cem\u003eMedicina (Kaunas)\u003c/em\u003e 2023, \u003cb\u003e59\u003c/b\u003e(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Busch F, Hoffmann L, Truhn D, Palaian S, Alomar M, Shpati K, Makowski MR, Bressem KK, Adams LC: \u003cb\u003eInternational pharmacy students' perceptions towards artificial intelligence in medicine-A multinational, multicentre cross-sectional study\u003c/b\u003e. \u003cem\u003eBr J Clin Pharmacol\u003c/em\u003e 2024, \u003cb\u003e90\u003c/b\u003e(3):649\u0026ndash;661.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Alruwaili MM, Abuadas FH, Alsadi M, Alruwaili AN, Elsayed Ramadan OM, Shaban M, Al Thobaity A, Alkahtani SM, El Arab RA: \u003cb\u003eExploring nurses' awareness and attitudes toward artificial intelligence: Implications for nursing practice\u003c/b\u003e. \u003cem\u003eDigit Health\u003c/em\u003e 2024, \u003cb\u003e10\u003c/b\u003e:20552076241271803.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Pan G, Ni J: \u003cb\u003eA cross sectional investigation of ChatGPT-like large language models application among medical students in China\u003c/b\u003e. \u003cem\u003eBMC Med Educ\u003c/em\u003e 2024, \u003cb\u003e24\u003c/b\u003e(1):908.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Jebreen K, Radwan E, Kammoun-Rebai W, Alattar E, Radwan A, Safi W, Radwan W, Alajez M: \u003cb\u003ePerceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine\u003c/b\u003e. \u003cem\u003eBMC Medical Education\u003c/em\u003e 2024, \u003cb\u003e24\u003c/b\u003e(1):507.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Sarfaraz S, Khurshid Z, Zafar MS: \u003cb\u003eUse of artificial intelligence in medical education: A strength or an infirmity\u003c/b\u003e. \u003cem\u003eJ Taibah Univ Med Sci\u003c/em\u003e 2023, \u003cb\u003e18\u003c/b\u003e(6):1553\u0026ndash;1554.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Jong M: \u003cb\u003eTransforming medical education in the AI era: Balancing technological expertise with humanistic care in tomorrow\u0026rsquo;s doctors\u003c/b\u003e. \u003cem\u003eAnn Acad Med Singap 2024\u003c/em\u003e 2024, \u003cb\u003e53\u003c/b\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Fazlollahi AM, Bakhaidar M, Alsayegh A, Yilmaz R, Winkler-Schwartz A, Mirchi N, Langleben I, Ledwos N, Sabbagh AJ, Bajunaid K \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eEffect of Artificial Intelligence Tutoring vs Expert Instruction on Learning Simulated Surgical Skills Among Medical Students: A Randomized Clinical Trial\u003c/b\u003e. \u003cem\u003eJAMA Netw Open\u003c/em\u003e 2022, \u003cb\u003e5\u003c/b\u003e(2):e2149008.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Alkaissi H, McFarlane SI: \u003cb\u003eArtificial Hallucinations in ChatGPT: Implications in Scientific Writing\u003c/b\u003e. \u003cem\u003eCureus\u003c/em\u003e 2023, \u003cb\u003e15\u003c/b\u003e(2):e35179.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Aljamaan F, Temsah MH, Altamimi I, Al-Eyadhy A, Jamal A, Alhasan K, Mesallam TA, Farahat M, Malki KH: \u003cb\u003eReference Hallucination Score for Medical Artificial Intelligence Chatbots: Development and Usability Study\u003c/b\u003e. \u003cem\u003eJMIR Med Inform\u003c/em\u003e 2024, \u003cb\u003e12\u003c/b\u003e:e54345.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Temsah MH, Alhuzaimi AN, Almansour M, Aljamaan F, Alhasan K, Batarfi MA, Altamimi I, Alharbi A, Alsuhaibani AA, Alwakeel L \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eArt or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL\u0026middot;E 3 for Illustrating Congenital Heart Diseases\u003c/b\u003e. \u003cem\u003eJ Med Syst\u003c/em\u003e 2024, \u003cb\u003e48\u003c/b\u003e(1):54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Stewart J, Lu J, Gahungu N, Goudie A, Fegan PG, Bennamoun M, Sprivulis P, Dwivedi G: \u003cb\u003eWestern Australian medical students' attitudes towards artificial intelligence in healthcare\u003c/b\u003e. \u003cem\u003ePLoS One\u003c/em\u003e 2023, \u003cb\u003e18\u003c/b\u003e(8):e0290642.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Sezgin E: \u003cb\u003eArtificial intelligence in healthcare: Complementing, not replacing, doctors and healthcare providers\u003c/b\u003e. \u003cem\u003eDigit Health\u003c/em\u003e 2023, \u003cb\u003e9\u003c/b\u003e:20552076231186520.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Zarei M, Eftekhari Mamaghani H, Abbasi A, Hosseini M-S: \u003cb\u003eApplication of artificial intelligence in medical education: A review of benefits, challenges, and solutions\u003c/b\u003e. \u003cem\u003eMedicina Cl\u0026iacute;nica Pr\u0026aacute;ctica\u003c/em\u003e 2024, \u003cb\u003e7\u003c/b\u003e(2):100422.\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, Medical education, ChatGPT, Undergraduate, Medical students, Saudi Arabia, AI adoption.","lastPublishedDoi":"10.21203/rs.3.rs-6222830/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6222830/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003cbr\u003e\nThe rapid evolution of Generative Artificial Intelligence (AI), particularly ChatGPT and large language models (LLMs), has introduced transformative potential in medical education. These tools offer innovative approaches to learning, simulation, and assessment. However, their integration into medical education remains underexplored, particularly in developing regions like Saudi Arabia. This study investigates medical students’ perceptions and attitudes toward AI in undergraduate medical education.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003cbr\u003e\nA cross-sectional survey was conducted among 1,039 undergraduate medical students across Saudi Arabia. The survey, validated through pilot testing, assessed students' familiarity with AI, perceptions of its role in medical education, and acceptance of AI-driven teaching. Statistical analyses, including logistic regression, identified factors influencing students' perceptions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003cbr\u003e\nAmong participants, 57.2% were familiar with AI's role in medical education, and 70.1% supported integrating AI into their curriculum. Additionally, 86.4% believed AI would impact the future of medical education, and 71.1% felt access to AI chatbots would influence their competency. While 73.4% saw AI as beneficial for basic science education, only 41.6% recognized its potential for clinical training. Concerns included trust in AI-generated content (47.4%) and issues like reference fabrication (64%). Only 29.8% viewed AI as superior to traditional methods, yet 60.7% believed it would enhance academic performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003cbr\u003e\nSaudi medical students show strong interest in AI integration, especially for basic sciences and simulation-based learning. However, they express skepticism about AI’s reliability and its ability to replace traditional tutor-based education. Concerns about ethical use and quality assurance highlight the need for structured guidelines to ensure AI is effectively incorporated while preserving critical human skills, clinical acumen, and ethical decision-making. Balancing AI with human instruction remains essential for its successful adoption in medical education.\u003c/p\u003e","manuscriptTitle":"Generative Artificial Intelligence Integration in Medical Education: A Cross-Sectional Survey of Medical Students’ Perceptions and Attitudes in Saudi Arabia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 13:13:36","doi":"10.21203/rs.3.rs-6222830/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":"f2d0a050-77fb-4c54-9f71-726c545f2ca6","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-26T10:09:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 13:13:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6222830","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6222830","identity":"rs-6222830","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}