Artificial Intelligence in Medical Education- Perception Among Medical Students

Artificial Intelligence in Medical Education- Perception Among Medical Students | 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 Artificial Intelligence in Medical Education- Perception Among Medical Students Preetha Jackson, Gayathri P S, Chikku Babu, Christa Tony, Deen Stephano Jack, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3833999/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Jul, 2024 Read the published version in BMC Medical Education → Version 1 posted 11 You are reading this latest preprint version Abstract Background Artificial Intelligence( AI) is increasingly being integrated into various aspects of human life, including healthcare, with applications such as robotic surgery, virtual nursing assistants, and image analysis. Recognizing the transformative impact of AI in healthcare, the World Medical Association advocates for the inclusion of AI education in medical curricula to prepare healthcare professionals for this emerging field. This study aims to assess medical students' perceptions on AI in medicine, their preferences for structured AI training during medical education, and their understanding of the ethical dimensions associated with AI in healthcare. Materials & Methods A cross-sectional study was conducted among 325 medical students in Kerala, India using a pre-validated, semi-structured, self- administered questionnaire. The survey collected demographic information, assessed participants' prior knowledge of AI, and evaluated their self-perceived understanding of AI concepts. Participants' responded to twelve 5-point Likert scale questions regarding their perceptions on AI in medicine and expressed their opinions on the inclusion of certain AI topics in medical curricula. Results & Discussion Most participants (57.2%) viewed AI as an assistive technology, capable of reducing errors in medical practice. A significant percentage(54.2%) believed that AI could enhance the accuracy of medical decisions, while 48.6% acknowledged its potential to improve patient access to healthcare. Concerns were raised by 37.6% of participants' about the potential decrease in the need for physicians, leading to unemployment. Additionally, apprehensions were expressed regarding the impact of AI on the humanistic aspects of medicine, with 69.2% fearing a decline in the human touch. Participants' also recognized potential challenges to "trust"( 52.9%), and the patient- physician relationship(54.5%). Notably, over half of the participants' were uncertain about maintaining professional confidentiality(51.1%) and believed that AI might violate confidentiality( 53.5%). Only 3.7% felt competent enough to inform patients' about features and risks of AI. Participants' expressed a strong need for structured training in AI applications, especially on the topic of "reducing medical errors"( 76.9%), and "ethical issues" arising from the widespread use of AI in healthcare(79.4%). Conclusion This study underscores the demand among medical students for structured AI training within the undergraduate medical curriculum, emphasizing the importance of incorporating AI education to meet evolving healthcare needs. While there are widespread ethical concerns, the majority are convinced that AI can be used as an assistive technology in healthcare. The findings contribute essential insights for curriculum development and the definition of learning outcomes in AI education for medical students. Artificial Intelligence Medical curriculum Healthcare Medical ethics Medical education Figures Figure 1 Figure 2 Introduction The concept of Artificial Intelligence (AI) dates back to the 1950s when Alan Turing, often referred to as the father of computer science, proposed the question, “Can machines think”? Interestingly, he designed the now famous ‘Turing Test’ where humans were to identify the responder of a question as human or machine ( 1 ). Subsequently in 1956 John McCarthy coined the term “Artificial Intelligence”( 2 ) and the next decade saw the birth of the first ever artificial neural network which was “the first machine which is capable of having an original idea” ( 3 ). Thus progressed the growth of this once unimaginable phenomenon. In this 21st century, most people are familiar with the term AI because of Siri (Intelligent Virtual Assistant) ( 4 ), Open AI’s ChatGPT (language model based chatbot) ( 5 ), traffic prediction by Google Maps or Uber ( 6 ) or customer service bots (AI powered assistants) ( 4 ) that intelligently provide suggestions. There is no universally accepted definition for AI, but it can be simply defined as “the ability of machines to mimic intelligent human behavior, including problem solving and learning” ( 7 ). Specific applications of AI include expert systems, natural language processing, speech recognition, machine vision, and many more, applying which AI has exhibited qualities similar to or even above those of humans ( 8 ). The use of AI and related technologies is becoming increasingly prevalent in all aspects of human life and beginning to influence the field of healthcare too ( 9 ). AI technologies have already developed algorithms to analyze a variety of health data, including clinical, behavioral, environmental, and drug information using data from both patients as well as biomedical literature ( 10 ). Convoluted Neural Networks, designed to automatically and adaptively learn spatial hierarchies of features, can be successfully used to develop diabetic retinopathy screening ( 11 ), skin lesion classification ( 12 ), lymph node metastasis detection( 13 ), and detection of an abnormality in a radiograph ( 14 ). Artificial Intelligence can help patients understand their symptoms, influence health seeking behavior, and thereby improve their quality of life ( 15 ). AI assistants have even suggested medicines for cancer patients with equal or better efficiency than human experts ( 16 ). With a capable AI assistant, it is possible to sift through and analyze multitudes of data in a matter of seconds and make conclusions, thus exponentially increasing its applications in biomedical research. AI promises future influences in healthcare in terms of AI assisted robotic surgery, virtual nursing assistants, and image analysis. Simply put, AI can help patients and healthcare providers in diagnosing a disease, assessing risk of disease, estimating treatment success, managing complications, and supporting patients ( 17 ). Artificial Intelligence though has limitless potential, just like a human brain, has its vulnerabilities and weaknesses. The quality and relevant information content of the input data to the deep learning model can affect the accuracy of diagnosis. The kind of funding that is required to construct the machinery and develop an intelligence is not easily accessible in the field of medicine, not to mention the constraints of machine ethics and confidentiality. However, being familiar with the concepts, applications, and advantages of AI is definitely beneficial and therefore advisable, especially in the field of medical education and policy making ( 17 , 18 ). The World Medical Association advocates for a change in medical curricula and educational opportunities for patients, physicians, medical students, health administrators, and other health care professionals to foster a better understanding of the numerous aspects of the healthcare AI, both positive and negative ( 19 ). Additionally, in 2019, the Standing Committee of European Doctors stressed the need to use AI systems in basic and continuing medical education ( 20 ). They recommended the need for AI systems to be integrated into medical education, residency training, and continuing medical education courses to increase awareness of the proper use of AI. In this context, there is a need for developing curricula specifically designed to train future physicians on AI. To develop an effective AI curriculum, we need to understand how today’s medical students perceive AI in medicine, and their comprehension of AI’s ethical dimension as well. However, the available need assessment studies are barely enough. Grunhut et al. recommend that national surveys need to be carried out among medical students on the attitude and expectations of learning AI in medical colleges for developing a curriculum. Such surveys should identify the realistic goals physicians will be asked to meet, the expectations that will be put on them, and the resources and knowledge they would need to meet these goals ( 21 ). Also, current literature falls short of a comprehensive needs assessment. It remains uncertain whether medical students are actually concerned that AI could significantly affect the current practice of clinical and academic medicine in the future. This possible transformation has accelerated with the latest pandemic and a change in medical education and healthcare service delivery is the need of the hour. This study can be a roadmap to a structured or standardized education on AI in which medical students might currently feel ignorant or inadequate about. Therefore, this can contribute to need assessment which is important for curriculum development and defining learning outcomes. In fact, there was only limited literature that studied perception of AI from a medical students’ point of view. Hence in this study we aimed to assess the perceptions on AI in medicine among medical students, to assess the proportion of medical students who are in favour of structured training on AI applications during their undergraduate course, and also to assess their perceptions on AI’s ethical dimensions. Methods Recruitment : A cross-sectional study was conducted among all the undergraduate medical students of Pushpagiri Institute of Medical Sciences and Research Centre during the period of June – August 2023. Participants who did not consent or submitted incomplete questionnaires were excluded from the study. An online survey using Google forms was conducted using a validated semi structured questionnaire which had 3 sections. The questions were adopted from a Turkish study by Civaner et al ( 22 ). The first section dealt with demographic details, any past educational experience about AI and participants’ self- evaluation of their knowledge of AI. The second section consisted of 12 five point Likert questions on medical students’ perception of AI including five questions on ethical aspects as well. The last section was about their thoughts on which topics about AI should be included in medical education. There were a total of 500 medical students in the Institute from 1st year MBBS to the medical students doing their internship. All medical students were invited to fill in the Google form. The Google form was open for 3 months, with reminder messages sent at intervals of one month. Participation was voluntary and informed consent was obtained through the first section of the Google form. A total of 327 medical students participated in the survey. After excluding the incomplete questionnaires, data of 325 participants were analyzed. Statistical Analysis: Responses on medical students’ perception on the possible influences of AI were graded using Likert scale ranging from 0 (totally disagree) to 4 (totally agree). Data was entered into Microsoft Excel. The quantitative variables were expressed as mean with standard deviation and categorical variables as percentage. Results AI in medicine- Prior knowledge and self-evaluation The mean (SD) age of the participants was 21.4 (1.9) (ranging from 18 to 25 years) with 76% females. Almost all (91.4%) of the participants reported that they have not received any form of training in AI while 52% students have heard about AI but possess no knowledge of it. About 32.6% self-reported to have ‘partial knowledge’ on AI while none of them reported to be ‘very knowledgeable.’ Of all the participants only 37.2% did not agree with the opinion that AI could replace physicians; instead, they (85.3%) thought that it could be an assistant or a tool that would help them. About 37.6% of participants agreed that the use of AI would reduce the need for physicians and thus result in loss of jobs. More than half of the participants (53.2%) agreed that they would become better physicians with the widespread use of AI applications. A third of the participants (35.1%) stated that their choice of specialization would be influenced by how AI was used in that field. The majority of students (91.4%) stated that they had not received any training on AI in their medical curriculum, while the others mentioned that they had attended events like seminars and presentations on AI. Only 26.8% of participants feel competent enough to give information on AI to patients. More than half of the participants (51.1%) were unsure of protecting patient confidentiality while using AI. Perceptions on the possible influences of AI in medicine Regarding student perceptions on the possible influences of AI in medicine (Fig. 1 ), the highest agreement was observed on the item ‘reduces error in medical practice’ (72.3%) while the lowest agreement was on ‘devalues the medical profession’ (40.3%). Students were mostly in favour of applying AI in medicine because they felt it would enable them to make more accurate decisions (72%) and would facilitate patients’ access to healthcare (60.9%). There were 59.4% of participants who agreed that AI would facilitate patient education and 50.5% agreed that AI would allow the patient to increase their control over their own health. Need for training on AI in medical curriculum. Almost three-fourths of the participants were in favour of structured training on AI applications that should be given during medical education (74.8%). The participants thought that it was important to be trained on various topics related to AI in medicine (Fig. 2 ). The most frequent topics that they perceived necessary in this domain were knowledge and skills about AI applications (84.3%), training to prevent and solve ethical problems that may arise with AI applications (79.4%), and AI assisted risk analysis for diseases (78.8%). Ethical concerns regarding AI in medicine( Table 1 ) On the topic of disadvantages and risks of using AI in medicine 69.2% agreed that AI would reduce the humanistic aspect of the medical profession, 54.5% agreed that it could negatively affect the patient-physician relationship, 52.9% were concerned that using AI assisted applications can damage trust in patients while 53.5% thought that AI could possibly cause violations of professional confidentiality. Table 1 Opinions of medical students on ethical considerations of including AI in medicine Concerns Totally agree Mostly agree Unsure Mostly disagree Totally disagree Negatively affects patient-physician relationship 73 104 100 45 3 Devalues the medical profession 43 88 126 57 11 Damages trust 76 96 97 46 10 Reduces the humanistic aspect of the medical profession 114 111 62 33 5 Violations of professional confidentiality 49 125 125 20 16 Discussion Artificial Intelligence (AI) is a far encompassing technology where the functioning of the human brain is mimicked by machines, through the use of computer science technologies. It can be programmed to perceive emotions, make decisions, or act like human beings. The strong and weak types of artificial intelligence are based on the levels of intelligence computers are programmed at. At a superficial level, weak AI acts on the completion of specific tasks. Here, robots or computers can handle information but are not able to think for themselves. Strong AI is part of the machinery that functions at the level a human brain does. Since strong AI is still at the developmental stage, the focus of much qualitative research is on weak AI. The use of AI in medical education has been studied extensively worldwide, particularly the perceptions of medical professionals, and their dilemmas about the use of AI in their day to day work. Our research is focused on the perception of medical students about the use of Artificial Intelligence in medicine. The mean age of the medical students we studied was around 21 years and the majority of students studied were females. Most participants in our study (53.3%) agreed that AI could not replace the physical presence of physicians but could help them in their work. This was substantiated by the 2021 study conducted by Bisdas S et al on medical students from 63 countries that AI could work as a “partner” rather than as a “competitor” in their medical practice. A third of our participants (37.6%) felt that the use of AI would reduce the need for physicians and would result in a loss of job opportunities for them. D Pinto Dos Santos published a study in European Radiology in 2019 where a majority of participants (83%) felt that human radiologists would not be replaced by robots or computers ( 23 ). In fact, there are many studies which argue that rather than physicians becoming redundant because of AI, they would change their practice and become “managers” rather than “custodians of information”( 24 , 25 ). More than half the respondents in our study (53.3%) agreed that they would become better physicians with the widespread use of AI applications. It would seem that medical students were aware of the uses and fields in which AI would be used in medical education. Respondents from other studies felt that currently available AI systems would actually complement physicians’ decision making skills by synthesizing large amounts of medical literature in order to produce the most up-to-date medical protocols and evidence ( 26 – 29 ). Similarly, studies show that AI systems actually work by complementing medical practice, rather than competing with human minds. After all, human minds have designed artificial intelligence. A third of the participants (35.1%)in our research stated that their choice of specialization would be influenced by how AI was used in that field. Much has been written about how AI might replace specialists in the fields of radiology and pathology as perceived by medical doctors and students. These are specializations that use computers and digital algorithms more when compared to other medical specialties. A Canadian study published in 2019 by Bo Gong et al found that 67% of the respondents felt that AI would “ reduce the demand” for radiologists. Many of the medical students interviewed in this study said that the anxiety they felt about being “ displaced” by AI technologies in radiology would discourage them from considering the field for specialization ( 14 , 30 , 31 , 32 ). In fact, a paper published by Yurdasik et al in 2021 had respondents encouraging practitioners to move away from specializations that used AI ( 33 ). However, there were other studies that reported results encouraging radiologists to get exposed to AI technologies so as to lower the rates of “ imaging related medical errors” and “lessening time spent in reading films,” resulting in more time spent with patients. German medical students have shown a positive attitude towards AI and have reported “not being afraid of being replaced by AI” should they choose radiology as their specialization ( 23 ). Attitudes towards the choice of specializations being influenced by AI depended on where the person was viewing the problem from- as a student or as a specialist and also from the degree of familiarity they had with AI applications. More than half of the participants (53.3%) agreed that they would become better physicians with the widespread use of AI applications. This is in concurrence with a recently published Western Australian study among medical students which showed about 75% of the participants agreeing that AI would improve their practice ( 34 ). A study by Paranjape et al ( 27 ) mentioned that at the time of publication of that paper, AI was being tried to assist in “faster and more accurate diagnosis, by computerized interpretation of algorithms in radiology, to cover for medical errors that might be caused by human fatigue, perform repetitive tasks and minimally invasive surgery” etc. The majority of the students (91.4%) stated that they had not received any training on AI in medicine. The American Medical Association meeting of 2018 on Augmented Intelligence advocated for the training of physicians so they could understand algorithms and work effectively with AI systems to make the best clinical care decisions for their patients ( 35 ). Despite this, Paranjape et al reported that training on the backend of electronic health record systems like, the quality of the data obtained, impact of computer use in front of patients, patient physician relationships etc. have not been addressed through medical education. If used with adequate training and understanding, AI will free up physicians’ time/ optimize a physician’s work hours, so that they can care and communicate with patients in the free time thus obtained. Medical curriculum does not address mathematical concepts (to understand algorithms), the fundamentals of AI like data science, or the ethical and legal issues that can come up with the use of AI ( 27 ). Only 25% of participants felt competent enough to give information on AI to patients. Unless medical physicians have a foundational understanding of AI, or the methods to critically appraise AI, they will be at a loss when called to train medical students on the use of AI tools in medical decision making. Consequently, medical students will be deficient in AI skills. Liaw et al advocate for Quintuple Competencies for the use of AI in primary health care, one of which is the need to understand how to communicate with patients regarding the why and how of the use of AI tools, privacy and confidentiality questions that patients may raise during patient physician interactions, and understand the emotional, trust or patient satisfaction issues that may arise because of AI use in health care( 36 ) More than half of the participants (51.1%) are unsure of protecting the confidentiality of patients during the use of AI technologies. Direct providers of health care need to be aware of what precautions to take when sharing data with third parties who are not the direct care providers to the patients ( 16 ). Artificial intelligence algorithms are derived from large data sets from human participants, and they may use data differently at different points in time. In such cases, patients can lose control of information they had consented to share especially where the impact on their privacy have not been adequately addressed. However much regulations might be made to protect patient confidentiality and privacy of data, they will always fall behind AI advances, which means the human brain has to work consistently to remain ahead of the artificial intelligence it created ( 37 ). Perceptions on the possible influences of AI in medicine The perceptions of medical students on the possible influences of AI in medicine were evaluated through the questionnaire. The highest agreement was found on the question, whether they thought the use of AI ‘reduces error in medical practice’ (72.3%) while the lowest agreement was on the question AI ‘devalues the medical profession’ (40.3%). Students were mostly in favor of the use of AI in medicine because they felt that it would enable them as physicians to make more accurate decisions (72%) and facilitate patients’ access to healthcare (60.9%). Research by Topol et al and Sharique et al have shown that AI technologies can help reduce medical errors by improving data flow patterns and improving diagnostic accuracy ( 29 , 37 , 38 ). The study from Western Australian students mentioned above ( 34 ) showed 74.4% of the studied students agreeing that the use of AI would improve practice of medicine in general. It is encouraging to find that medical students in this research showed low agreement when asked if AI would devalue the medical profession and agreed that the use of AI would reduce medical errors caused inadvertently. It should also be noted that some research has shown that the inappropriate use of AI itself can introduce errors in medical practice ( 39 ). On “disadvantages and risks of AI in medicine”, 69.2% of the students agreed that AI would reduce the humanistic aspect of the medical profession, 54.5% agreed that it can negatively affect the patient-physician relationship, 52.9% were concerned that using AI assisted applications could damage the trust patients placed on physicians, 59.4% agreed that AI would facilitate patient education, and 50.5% agreed that AI would allow the patient to increase their control over their own health. Hadithy et al (2023) found that students believed AI technology was advantageous for improving overall health by personalizing health care through analyzing patient information ( 40 ). Medical education in the 21st century is swiftly transitioning from the conventional approach of observing patients objectively from a distance and holding the belief that compassion is an innate skill to a contemporary paradigm. This new model emphasizes the development of competencies such as doctor-patient relationships, communication skills, and professionalism. In modern medicine, AI is being viewed as an additional barrier between a patient and his physician. Machines have many advantages over humans as rightly observed by Wartman especially in view of not being affected by many of the human frailties like fatigue, information overload, inability to retain material beyond a limit etc. ( 24 ). Skepticism over the use of AI in medical practice often stems from the lack of knowledge in this domain. Medical students, in many studies, opined that classes on artificial intelligence need to be included in syllabus, but only very few medical schools have included these in their medical curricula. Practicing with compassion and empathy must be a taught and cultivated skill along with artificial intelligence. The two should go together, taught in tandem throughout the medical course. Studies such as this have highlighted that students are open to being taught but are deficient in the skills and knowledge. There is a gap here that needs to be addressed. Man, and machine have to work as partners so as to improve the health of the people. Limitations: Though this research was one of the first conducted in the state of Kerala and covered about 65 percent of medical students of the institution, which is more than other similar surveys conducted, there are a few limitations that have been identified. As an online survey method using Google Forms was implied for data collection, the voluntary nature of the participation from only those who were interested, might have introduced a self-selection bias and a non-response bias in this research. As this study only includes the responses from the medical students of one institution, it might not have captured a wide variety of responses. Hence the generalizability of the study may be limited. From the literature review we could not identify another measurement tool other than the one we used which reflects the need for more studies on similar populations. Three - fourth of our respondents were females indicating a gender bias corresponding to gender distributions of students in medical colleges of Kerala. The questionnaire did not delve deep into how AI terms are understood, or how proficient students were with AI and so might have missed more relevant AI terms and concepts that students might be unfamiliar with. Most data collected in this study were quantitative so we might not have captured the depth of the students' understanding or perceptions about AI. As many of the students had no exposure to computer science or had not attended AI classes, their perceptions might have been influenced by lack of exposure. Thus, the study might not have captured the views of those who had a more informed background on the subject. Future studies are recommended to replicate and validate the findings in larger and more diverse populations to understand regional variations in knowledge, attitude, and perceptions among medical students. This study tool (questionnaire) was adopted from a parent study by Civaner M M ( 10 ), but the last question on the need for any other topic to be included was not met with enthusiasm. Conclusion This exploration into the perceptions of medical students regarding the integration of Artificial Intelligence (AI) into medical education reveals a nuanced landscape. The majority of participants in this study recognize the collaborative potential of AI, viewing it not as a replacement for physicians but as a valuable ally in healthcare. Interestingly, concerns on job displacement coexist with the optimism about improved decision-making and enhanced medical practice. The knowledge deficit in this context can extend an incompetence in communicating AI related information to patients, highlighting the urgent need for a holistic approach to medical education. The findings complement the perceived need of a proactive approach in preparing medical students for a future where AI plays a pivotal role in healthcare, ensuring that they not only embrace technological advancements but also uphold the humanistic values inherent to the practice of medicine. Abbreviations AI : Artificial Intelligence IIT : Indian Institute of Technology Declarations Ethics Approval and consent to participate : Ethical approval of the study was obtained from the Institutional Ethics Committee of Pushpagiri Institute of Medical Sciences and Research Centre (Dated: 29 June 2023 No. PIMSRC /E1/388A/72/2023). Informed Consent : Informed consent to participate was obtained from each participant through Google Forms after information about the study was provided. Consent for publication : Not applicable Availability of data and materials : The data set is provided as supplementary information and the data sets used and/or analysed during the current study are available from the corresponding authors (Anjum John or Preetha Jackson) upon reasonable request. Competing Interests : The authors can identify no competing interests with regard to this research or publication. Funding : There has been no funding provided for this research or publication so far. Author’s contributions : Preetha Jackson – Design, acquisition, analysis, interpretation of data, drafting of the work, and revisions. Gayathri P.S – Acquisition, analysis, interpretation of data, substantially revising the work and editing revisions. Dency Davis – Acquisition, interpretation of data, substantially revising the work. Chinnu Babu, Christa Tony M , Deen Stephano Jack – acquisition and reading the manuscript. Reshma V.R- Analysis, Interpretation of data, reading the manuscript and substantial revisions. Nisha Kurian- Substantially revising the work, manuscript, correcting final draft before submission. Anjum John- Conception, design, acquisition, drafting of the work, and substantially contributing to revising the work, correcting final draft and submission. All authors : Reviewed and approved the final manuscript before publication. Acknowledgements : We acknowledge the assistance of Dr. Rosin George Varghese and Dr. Joe Abraham (Assistant Professors, Community Medicine, Pushpagiri Institute of Medical Sciences and Research Centre) in reviewing the protocol and suggesting modifications and Dr. Felix Johns (Professor and Head, Community Medicine, Pushpagiri Institute of Medical Sciences and Research Centre) in supporting us throughout this research. We are also thankful to Civaner and the team for kindly providing us with the questionnaire for this research and being available to answer any questions we had. Author’s information : Preetha Jackson, Gayathri P.S, Dency Davis- Graduate Students of Community Medicine, Pushpagiri Institute of Medical Sciences, Tiruvalla, Kerala, India Chikku Babu, Christa Tony M, Deen Stephano Jack – house surgeons in training of the Community Medicine Department, Pushpagiri Institute of Medical Sciences, Tiruvalla, Kerala, India. Reshma V. R. Biostatistician, Community Medicine Department, Pushpagiri Institute of Medical Sciences, Tiruvalla, Kerala, India. Nisha Kurian – Assistant Professor of Biostatistics, Community Medicine Department, Pushpagiri Institute of Medical Sciences, Tiruvalla, Kerala, India. Anjum John- Assistant Professor of Community Medicine, Pushpagiri Institute of Medical Sciences, Tiruvalla, Kerala, India References McCarthy J. What is artificial intelligence? [Internet]. 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Medical students' attitude towards artificial intelligence: a multicentre survey. Eur Radiol. 2019;29(4):1640–6. 10.1007/s00330-018-5601-1 . Epub 2018 Jul 6. PMID: 29980928. Wartman SA, Combs CD. Medical Education Must Move From the Information Age to the Age of Artificial Intelligence. Acad Med. 2018;93(8):1107–1109. 10.1097/ACM.0000000000002044 . PMID: 29095704. Wartman SA, Medicine, Machines. and Medical Education. Acad Med. 2021;96(7):947–950. 10.1097/ACM.0000000000004113 . PMID: 33788788. Fabien Lareyre Cédric, Adam M, Carrier N, Chakfé. Juliette Raffort.Artificial Intelligence for Education of Vascular Surgeons.European Journal of Vascular and Endovascular Surgery,Volume 59, Issue 6,2020,Pages 870–871, -5884,https://doi.org/10.1016/j.ejvs.2020.02.030 .(https://www.sciencedirect.com/science/article/pii/S1078588420301659 ). Paranjape K, Schinkel M, Nannan Panday R, CAr J, Nanayakkara P. Introducing artificial intelligence training in medical education. JMIR Med Educ. 2019;5:e16048. Srivastava TK, Waghmere L, Mar. Vol. -14(3): JI01–2. Park SH, Do KH, Kim S, Park JH, Lim YS. What should medical students know about artificial intelligence in medicine? J Educ Eval Health Prof. 2019;16:18. 10.3352/jeehp.2019.16.18 . Epub 2019 Jul 3. PMID: 31319450; PMCID: PMC6639123. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500. Sit C, Srinivasan R, Amlani A, Muthuswamy K, Azam A, Monzon L et al. Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights Imaging [Internet]. 2020 Dec [cited 2023 Jul 10];11. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7002761/ . Gong B, Nugent JP, Guest W, Parker W, Chang PJ, Khosa F, Nicolaou S. Influence of Artificial Intelligence on Canadian Medical Students' Preference for Radiology Specialty: ANational Survey Study. Acad Radiol. 2019;26(4):566–577. 10.1016/j.acra.2018.10.007 . Epub 2018 Nov 11. PMID: 30424998. Yurdaisik I, Aksoy SH. Evaluation of knowledge and attitudes of radiology department workers about artificial intelligence. Ann Clin Anal Med. 2021;12:186–90. 10.4328/ACAM.20453 . Stewart J, Lu J, Gahungu N, Goudie A, Fegan PG, Bennamoun M, et al. Western Australian medical students’ attitudes towards artificial intelligence in healthcare. PLoS ONE. 2023;18(8):e0290642. https://doi.org/10.1371/journal.pone.0290642 . Augmented Intelligence in Health Care. AMA AI, Board Report. 2018. Available at https://www.ama-assn.org/system/files/2019-08/ai-2018-board-report.pdf . Liaw W, Kueper JK, Lin S, Bazemore A, Kakadiaris I. Competencies for the Use of Artificial Intelligence in Primary Care. Ann Fam Med. 2022 Nov-Dec;20(6):559–63. 10.1370/afm.2887 . PMID: 36443071; PMCID: PMC9705044. Murdoch B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics. 2021;22:122. https://doi.org/10.1186/s12910-021-00687-3 . Sharique Ahmad & Saeeda Wasim. Prevent Medical Errors through Artificial Intelligence: A Review. Saudi J Med Pharm Sci. 2023;9(7):419–23. Choudhury A, Asan O. Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review. JMIR Med Inform. 2020;8(7):e18599. 10.2196/18599 . PMID: 32706688; PMCID: PMC7414411. Al Hadithy ZA, Al Lawati A, Al-Zadjali R, Al Sinawi H. Knowledge, Attitudes, and Perceptions of Artificial Intelligence in Healthcare Among Medical Students at Sultan Qaboos University. Cureus. 2023;15(9):e44887. 10.7759/cureus.44887 . PMID: 37814766; PMCID: PMC10560391. Additional Declarations No competing interests reported. Supplementary Files ARTIFICIALINTELLIGENCEINMEDICALEDUCATIONPERCEPTIONAMONGMEDICALSTUDENTSResponses.xlsx AnnexuresAICONSENTQUESTIONNAIRE.docx Cite Share Download PDF Status: Published Journal Publication published 26 Jul, 2024 Read the published version in BMC Medical Education → Version 1 posted Editorial decision: Revision requested 12 Mar, 2024 Reviews received at journal 29 Feb, 2024 Reviewers agreed at journal 26 Feb, 2024 Reviewers agreed at journal 30 Jan, 2024 Reviews received at journal 28 Jan, 2024 Reviewers agreed at journal 28 Jan, 2024 Reviewers invited by journal 18 Jan, 2024 Editor assigned by journal 09 Jan, 2024 Editor invited by journal 09 Jan, 2024 Submission checks completed at journal 09 Jan, 2024 First submitted to journal 04 Jan, 2024 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. 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Interestingly, he designed the now famous \u0026lsquo;Turing Test\u0026rsquo; where humans were to identify the responder of a question as human or machine (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Subsequently in 1956 John McCarthy coined the term \u0026ldquo;Artificial Intelligence\u0026rdquo;(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) and the next decade saw the birth of the first ever artificial neural network which was \u0026ldquo;the first machine which is capable of having an original idea\u0026rdquo; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Thus progressed the growth of this once unimaginable phenomenon. In this 21st century, most people are familiar with the term AI because of Siri (Intelligent Virtual Assistant) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), Open AI\u0026rsquo;s ChatGPT (language model based chatbot) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), traffic prediction by Google Maps or Uber (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) or customer service bots (AI powered assistants) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) that intelligently provide suggestions.\u003c/p\u003e \u003cp\u003eThere is no universally accepted definition for AI, but it can be simply defined as \u0026ldquo;the ability of machines to mimic intelligent human behavior, including problem solving and learning\u0026rdquo; (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Specific applications of AI include expert systems, natural language processing, speech recognition, machine vision, and many more, applying which AI has exhibited qualities similar to or even above those of humans (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe use of AI and related technologies is becoming increasingly prevalent in all aspects of human life and beginning to influence the field of healthcare too (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). AI technologies have already developed algorithms to analyze a variety of health data, including clinical, behavioral, environmental, and drug information using data from both patients as well as biomedical literature (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Convoluted Neural Networks, designed to automatically and adaptively learn spatial hierarchies of features, can be successfully used to develop diabetic retinopathy screening (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), skin lesion classification (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), lymph node metastasis detection(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), and detection of an abnormality in a radiograph (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eArtificial Intelligence can help patients understand their symptoms, influence health seeking behavior, and thereby improve their quality of life (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). AI assistants have even suggested medicines for cancer patients with equal or better efficiency than human experts (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). With a capable AI assistant, it is possible to sift through and analyze multitudes of data in a matter of seconds and make conclusions, thus exponentially increasing its applications in biomedical research. AI promises future influences in healthcare in terms of AI assisted robotic surgery, virtual nursing assistants, and image analysis. Simply put, AI can help patients and healthcare providers in diagnosing a disease, assessing risk of disease, estimating treatment success, managing complications, and supporting patients (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eArtificial Intelligence though has limitless potential, just like a human brain, has its vulnerabilities and weaknesses. The quality and relevant information content of the input data to the deep learning model can affect the accuracy of diagnosis. The kind of funding that is required to construct the machinery and develop an intelligence is not easily accessible in the field of medicine, not to mention the constraints of machine ethics and confidentiality. However, being familiar with the concepts, applications, and advantages of AI is definitely beneficial and therefore advisable, especially in the field of medical education and policy making (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe World Medical Association advocates for a change in medical curricula and educational opportunities for patients, physicians, medical students, health administrators, and other health care professionals to foster a better understanding of the numerous aspects of the healthcare AI, both positive and negative (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Additionally, in 2019, the Standing Committee of European Doctors stressed the need to use AI systems in basic and continuing medical education (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). They recommended the need for AI systems to be integrated into medical education, residency training, and continuing medical education courses to increase awareness of the proper use of AI. In this context, there is a need for developing curricula specifically designed to train future physicians on AI.\u003c/p\u003e \u003cp\u003eTo develop an effective AI curriculum, we need to understand how today\u0026rsquo;s medical students perceive AI in medicine, and their comprehension of AI\u0026rsquo;s ethical dimension as well. However, the available need assessment studies are barely enough. Grunhut et al. recommend that national surveys need to be carried out among medical students on the attitude and expectations of learning AI in medical colleges for developing a curriculum. Such surveys should identify the realistic goals physicians will be asked to meet, the expectations that will be put on them, and the resources and knowledge they would need to meet these goals (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Also, current literature falls short of a comprehensive needs assessment.\u003c/p\u003e \u003cp\u003eIt remains uncertain whether medical students are actually concerned that AI could significantly affect the current practice of clinical and academic medicine in the future. This possible transformation has accelerated with the latest pandemic and a change in medical education and healthcare service delivery is the need of the hour. This study can be a roadmap to a structured or standardized education on AI in which medical students might currently feel ignorant or inadequate about. Therefore, this can contribute to need assessment which is important for curriculum development and defining learning outcomes. In fact, there was only limited literature that studied perception of AI from a medical students\u0026rsquo; point of view. Hence in this study we aimed to assess the perceptions on AI in medicine among medical students, to assess the proportion of medical students who are in favour of structured training on AI applications during their undergraduate course, and also to assess their perceptions on AI\u0026rsquo;s ethical dimensions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eRecruitment : A cross-sectional study was conducted among all the undergraduate medical students of Pushpagiri Institute of Medical Sciences and Research Centre during the period of June \u0026ndash; August 2023.\u003c/p\u003e \u003cp\u003eParticipants who did not consent or submitted incomplete questionnaires were excluded from the study. An online survey using Google forms was conducted using a validated semi structured questionnaire which had 3 sections. The questions were adopted from a Turkish study by Civaner et al (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe first section dealt with demographic details, any past educational experience about AI and participants\u0026rsquo; self- evaluation of their knowledge of AI. The second section consisted of 12 five point Likert questions on medical students\u0026rsquo; perception of AI including five questions on ethical aspects as well. The last section was about their thoughts on which topics about AI should be included in medical education.\u003c/p\u003e \u003cp\u003eThere were a total of 500 medical students in the Institute from 1st year MBBS to the medical students doing their internship. All medical students were invited to fill in the Google form. The Google form was open for 3 months, with reminder messages sent at intervals of one month. Participation was voluntary and informed consent was obtained through the first section of the Google form. A total of 327 medical students participated in the survey. After excluding the incomplete questionnaires, data of 325 participants were analyzed.\u003c/p\u003e \u003cp\u003eStatistical Analysis: Responses on medical students\u0026rsquo; perception on the possible influences of AI were graded using Likert scale ranging from 0 (totally disagree) to 4 (totally agree). Data was entered into Microsoft Excel. The quantitative variables were expressed as mean with standard deviation and categorical variables as percentage.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAI in medicine- Prior knowledge and self-evaluation\u003c/h2\u003e \u003cp\u003eThe mean (SD) age of the participants was 21.4 (1.9) (ranging from 18 to 25 years) with 76% females. Almost all (91.4%) of the participants reported that they have not received any form of training in AI while 52% students have heard about AI but possess no knowledge of it. About 32.6% self-reported to have \u0026lsquo;partial knowledge\u0026rsquo; on AI while none of them reported to be \u0026lsquo;very knowledgeable.\u0026rsquo;\u003c/p\u003e \u003cp\u003eOf all the participants only 37.2% did not agree with the opinion that AI could replace physicians; instead, they (85.3%) thought that it could be an assistant or a tool that would help them. About 37.6% of participants agreed that the use of AI would reduce the need for physicians and thus result in loss of jobs. More than half of the participants (53.2%) agreed that they would become better physicians with the widespread use of AI applications. A third of the participants (35.1%) stated that their choice of specialization would be influenced by how AI was used in that field. The majority of students (91.4%) stated that they had not received any training on AI in their medical curriculum, while the others mentioned that they had attended events like seminars and presentations on AI. Only 26.8% of participants feel competent enough to give information on AI to patients. More than half of the participants (51.1%) were unsure of protecting patient confidentiality while using AI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePerceptions on the possible influences of AI in medicine\u003c/h2\u003e \u003cp\u003eRegarding student perceptions on the possible influences of AI in medicine (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the highest agreement was observed on the item \u0026lsquo;reduces error in medical practice\u0026rsquo; (72.3%) while the lowest agreement was on \u0026lsquo;devalues the medical profession\u0026rsquo; (40.3%). Students were mostly in favour of applying AI in medicine because they felt it would enable them to make more accurate decisions (72%) and would facilitate patients\u0026rsquo; access to healthcare (60.9%). There were 59.4% of participants who agreed that AI would facilitate patient education and 50.5% agreed that AI would allow the patient to increase their control over their own health.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNeed for training on AI in medical curriculum.\u003c/span\u003e \u003c/p\u003e \u003cp\u003eAlmost three-fourths of the participants were in favour of structured training on AI applications that should be given during medical education (74.8%). The participants thought that it was important to be trained on various topics related to AI in medicine (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The most frequent topics that they perceived necessary in this domain were knowledge and skills about AI applications (84.3%), training to prevent and solve ethical problems that may arise with AI applications (79.4%), and AI assisted risk analysis for diseases (78.8%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEthical concerns regarding AI in medicine( Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eOn the topic of disadvantages and risks of using AI in medicine 69.2% agreed that AI would reduce the humanistic aspect of the medical profession, 54.5% agreed that it could negatively affect the patient-physician relationship, 52.9% were concerned that using AI assisted applications can damage trust in patients while 53.5% thought that AI could possibly cause violations of professional confidentiality.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOpinions of medical students on ethical considerations of including AI in medicine\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcerns\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotally agree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMostly agree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnsure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMostly disagree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotally disagree\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegatively affects patient-physician relationship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevalues the medical profession\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDamages trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReduces the humanistic aspect of the medical profession\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViolations of professional confidentiality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eArtificial Intelligence (AI) is a far encompassing technology where the functioning of the human brain is mimicked by machines, through the use of computer science technologies. It can be programmed to perceive emotions, make decisions, or act like human beings. The strong and weak types of artificial intelligence are based on the levels of intelligence computers are programmed at. At a superficial level, weak AI acts on the completion of specific tasks. Here, robots or computers can handle information but are not able to think for themselves. Strong AI is part of the machinery that functions at the level a human brain does. Since strong AI is still at the developmental stage, the focus of much qualitative research is on weak AI. The use of AI in medical education has been studied extensively worldwide, particularly the perceptions of medical professionals, and their dilemmas about the use of AI in their day to day work. Our research is focused on the perception of medical students about the use of Artificial Intelligence in medicine.\u003c/p\u003e \u003cp\u003eThe mean age of the medical students we studied was around 21 years and the majority of students studied were females. Most participants in our study (53.3%) agreed that AI could not replace the physical presence of physicians but could help them in their work. This was substantiated by the 2021 study conducted by Bisdas S et al on medical students from 63 countries that AI could work as a \u0026ldquo;partner\u0026rdquo; rather than as a \u0026ldquo;competitor\u0026rdquo; in their medical practice. A third of our participants (37.6%) felt that the use of AI would reduce the need for physicians and would result in a loss of job opportunities for them. D Pinto Dos Santos published a study in European Radiology in 2019 where a majority of participants (83%) felt that human radiologists would not be replaced by robots or computers (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In fact, there are many studies which argue that rather than physicians becoming redundant because of AI, they would change their practice and become \u0026ldquo;managers\u0026rdquo; rather than \u0026ldquo;custodians of information\u0026rdquo;(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMore than half the respondents in our study (53.3%) agreed that they would become better physicians with the widespread use of AI applications. It would seem that medical students were aware of the uses and fields in which AI would be used in medical education. Respondents from other studies felt that currently available AI systems would actually complement physicians\u0026rsquo; decision making skills by synthesizing large amounts of medical literature in order to produce the most up-to-date medical protocols and evidence (\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Similarly, studies show that AI systems actually work by complementing medical practice, rather than competing with human minds. After all, human minds have designed artificial intelligence.\u003c/p\u003e \u003cp\u003eA third of the participants (35.1%)in our research stated that their choice of specialization would be influenced by how AI was used in that field. Much has been written about how AI might replace specialists in the fields of radiology and pathology as perceived by medical doctors and students. These are specializations that use computers and digital algorithms more when compared to other medical specialties. A Canadian study published in 2019 by Bo Gong et al found that 67% of the respondents felt that AI would \u0026ldquo; reduce the demand\u0026rdquo; for radiologists. Many of the medical students interviewed in this study said that the anxiety they felt about being \u0026ldquo; displaced\u0026rdquo; by AI technologies in radiology would discourage them from considering the field for specialization (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In fact, a paper published by Yurdasik et al in 2021 had respondents encouraging practitioners to move away from specializations that used AI (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). However, there were other studies that reported results encouraging radiologists to get exposed to AI technologies so as to lower the rates of \u0026ldquo; imaging related medical errors\u0026rdquo; and \u0026ldquo;lessening time spent in reading films,\u0026rdquo; resulting in more time spent with patients. German medical students have shown a positive attitude towards AI and have reported \u0026ldquo;not being afraid of being replaced by AI\u0026rdquo; should they choose radiology as their specialization (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Attitudes towards the choice of specializations being influenced by AI depended on where the person was viewing the problem from- as a student or as a specialist and also from the degree of familiarity they had with AI applications.\u003c/p\u003e \u003cp\u003eMore than half of the participants (53.3%) agreed that they would become better physicians with the widespread use of AI applications. This is in concurrence with a recently published Western Australian study among medical students which showed about 75% of the participants agreeing that AI would improve their practice (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). A study by Paranjape et al (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) mentioned that at the time of publication of that paper, AI was being tried to assist in \u0026ldquo;faster and more accurate diagnosis, by computerized interpretation of algorithms in radiology, to cover for medical errors that might be caused by human fatigue, perform repetitive tasks and minimally invasive surgery\u0026rdquo; etc.\u003c/p\u003e \u003cp\u003eThe majority of the students (91.4%) stated that they had not received any training on AI in medicine. The American Medical Association meeting of 2018 on Augmented Intelligence advocated for the training of physicians so they could understand algorithms and work effectively with AI systems to make the best clinical care decisions for their patients (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Despite this, Paranjape et al reported that training on the backend of electronic health record systems like, the quality of the data obtained, impact of computer use in front of patients, patient physician relationships etc. have not been addressed through medical education. If used with adequate training and understanding, AI will free up physicians\u0026rsquo; time/ optimize a physician\u0026rsquo;s work hours, so that they can care and communicate with patients in the free time thus obtained.\u003c/p\u003e \u003cp\u003eMedical curriculum does not address mathematical concepts (to understand algorithms), the fundamentals of AI like data science, or the ethical and legal issues that can come up with the use of AI (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Only 25% of participants felt competent enough to give information on AI to patients. Unless medical physicians have a foundational understanding of AI, or the methods to critically appraise AI, they will be at a loss when called to train medical students on the use of AI tools in medical decision making. Consequently, medical students will be deficient in AI skills. Liaw et al advocate for Quintuple Competencies for the use of AI in primary health care, one of which is the need to understand how to communicate with patients regarding the why and how of the use of AI tools, privacy and confidentiality questions that patients may raise during patient physician interactions, and understand the emotional, trust or patient satisfaction issues that may arise because of AI use in health care(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eMore than half of the participants (51.1%) are unsure of protecting the confidentiality of patients during the use of AI technologies. Direct providers of health care need to be aware of what precautions to take when sharing data with third parties who are not the direct care providers to the patients (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Artificial intelligence algorithms are derived from large data sets from human participants, and they may use data differently at different points in time. In such cases, patients can lose control of information they had consented to share especially where the impact on their privacy have not been adequately addressed. However much regulations might be made to protect patient confidentiality and privacy of data, they will always fall behind AI advances, which means the human brain has to work consistently to remain ahead of the artificial intelligence it created (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePerceptions on the possible influences of AI in medicine\u003c/h2\u003e \u003cp\u003eThe perceptions of medical students on the possible influences of AI in medicine were evaluated through the questionnaire. The highest agreement was found on the question, whether they thought the use of AI \u0026lsquo;reduces error in medical practice\u0026rsquo; (72.3%) while the lowest agreement was on the question AI \u0026lsquo;devalues the medical profession\u0026rsquo; (40.3%).\u003c/p\u003e \u003cp\u003eStudents were mostly in favor of the use of AI in medicine because they felt that it would enable them as physicians to make more accurate decisions (72%) and facilitate patients\u0026rsquo; access to healthcare (60.9%). Research by Topol et al and Sharique et al have shown that AI technologies can help reduce medical errors by improving data flow patterns and improving diagnostic accuracy (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The study from Western Australian students mentioned above (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) showed 74.4% of the studied students agreeing that the use of AI would improve practice of medicine in general. It is encouraging to find that medical students in this research showed low agreement when asked if AI would devalue the medical profession and agreed that the use of AI would reduce medical errors caused inadvertently. It should also be noted that some research has shown that the inappropriate use of AI itself can introduce errors in medical practice (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn \u0026ldquo;disadvantages and risks of AI in medicine\u0026rdquo;, 69.2% of the students agreed that AI would reduce the humanistic aspect of the medical profession, 54.5% agreed that it can negatively affect the patient-physician relationship, 52.9% were concerned that using AI assisted applications could damage the trust patients placed on physicians, 59.4% agreed that AI would facilitate patient education, and 50.5% agreed that AI would allow the patient to increase their control over their own health. Hadithy et al (2023) found that students believed AI technology was advantageous for improving overall health by personalizing health care through analyzing patient information (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMedical education in the 21st century is swiftly transitioning from the conventional approach of observing patients objectively from a distance and holding the belief that compassion is an innate skill to a contemporary paradigm. This new model emphasizes the development of competencies such as doctor-patient relationships, communication skills, and professionalism. In modern medicine, AI is being viewed as an additional barrier between a patient and his physician. Machines have many advantages over humans as rightly observed by Wartman especially in view of not being affected by many of the human frailties like fatigue, information overload, inability to retain material beyond a limit etc. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Skepticism over the use of AI in medical practice often stems from the lack of knowledge in this domain. Medical students, in many studies, opined that classes on artificial intelligence need to be included in syllabus, but only very few medical schools have included these in their medical curricula. Practicing with compassion and empathy must be a taught and cultivated skill along with artificial intelligence. The two should go together, taught in tandem throughout the medical course. Studies such as this have highlighted that students are open to being taught but are deficient in the skills and knowledge. There is a gap here that needs to be addressed. Man, and machine have to work as partners so as to improve the health of the people.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eLimitations:\u003c/h2\u003e \u003cp\u003eThough this research was one of the first conducted in the state of Kerala and covered about 65 percent of medical students of the institution, which is more than other similar surveys conducted, there are a few limitations that have been identified. As an online survey method using Google Forms was implied for data collection, the voluntary nature of the participation from only those who were interested, might have introduced a self-selection bias and a non-response bias in this research. As this study only includes the responses from the medical students of one institution, it might not have captured a wide variety of responses. Hence the generalizability of the study may be limited. From the literature review we could not identify another measurement tool other than the one we used which reflects the need for more studies on similar populations. Three - fourth of our respondents were females indicating a gender bias corresponding to gender distributions of students in medical colleges of Kerala. The questionnaire did not delve deep into how AI terms are understood, or how proficient students were with AI and so might have missed more relevant AI terms and concepts that students might be unfamiliar with. Most data collected in this study were quantitative so we might not have captured the depth of the students' understanding or perceptions about AI. As many of the students had no exposure to computer science or had not attended AI classes, their perceptions might have been influenced by lack of exposure. Thus, the study might not have captured the views of those who had a more informed background on the subject.\u003c/p\u003e \u003cp\u003eFuture studies are recommended to replicate and validate the findings in larger and more diverse populations to understand regional variations in knowledge, attitude, and perceptions among medical students. This study tool (questionnaire) was adopted from a parent study by Civaner M M (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), but the last question on the need for any other topic to be included was not met with enthusiasm.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis exploration into the perceptions of medical students regarding the integration of Artificial Intelligence (AI) into medical education reveals a nuanced landscape. The majority of participants in this study recognize the collaborative potential of AI, viewing it not as a replacement for physicians but as a valuable ally in healthcare. Interestingly, concerns on job displacement coexist with the optimism about improved decision-making and enhanced medical practice. The knowledge deficit in this context can extend an incompetence in communicating AI related information to patients, highlighting the urgent need for a holistic approach to medical education. The findings complement the perceived need of a proactive approach in preparing medical students for a future where AI plays a pivotal role in healthcare, ensuring that they not only embrace technological advancements but also uphold the humanistic values inherent to the practice of medicine.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAI : Artificial Intelligence\u003c/p\u003e\n\u003cp\u003eIIT : Indian Institute of Technology\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics Approval and consent to participate :\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical approval of the study was obtained from the Institutional Ethics Committee of Pushpagiri Institute of Medical Sciences and Research Centre (Dated: 29 June 2023 No. PIMSRC /E1/388A/72/2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed Consent :\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Informed consent to participate was obtained from each participant through Google Forms after information about the study was provided.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication : Not applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials : The data set is provided as supplementary information and the data sets used and/or analysed during the current study are available from the corresponding authors (Anjum John or Preetha Jackson) upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting Interests : The authors can identify no competing interests with regard to this research or publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding : There has been no funding provided for this research or publication so far.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor\u0026rsquo;s contributions :\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePreetha Jackson \u0026ndash; Design, acquisition, analysis, interpretation of data, drafting of the work, and revisions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGayathri P.S \u0026ndash; Acquisition, analysis, interpretation of data, substantially revising the work and editing revisions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDency Davis \u0026ndash; Acquisition, interpretation of data, substantially revising the work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChinnu Babu, Christa Tony M , Deen Stephano Jack \u0026ndash; acquisition and reading the manuscript.\u003c/p\u003e\n\u003cp\u003eReshma V.R- Analysis, Interpretation of data, reading the manuscript and substantial revisions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNisha Kurian- Substantially revising the work, manuscript, correcting final draft before submission.\u003c/p\u003e\n\u003cp\u003eAnjum John- Conception, design, acquisition, drafting of the work, and substantially contributing to revising the work, correcting final draft and submission.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors : Reviewed and approved the final manuscript before publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgements : We acknowledge the assistance of Dr. Rosin George Varghese and Dr. Joe Abraham (Assistant Professors, Community Medicine, Pushpagiri Institute of Medical Sciences and Research Centre) in reviewing the protocol and suggesting modifications and Dr. Felix Johns (Professor and Head, Community Medicine, Pushpagiri Institute of Medical Sciences and Research Centre) in supporting us throughout this research. We are also thankful to Civaner and the team for kindly providing us with the questionnaire for this research and being available to answer any questions we had.\u003c/p\u003e\n\u003cp\u003eAuthor\u0026rsquo;s information :\u003c/p\u003e\n\u003cp\u003ePreetha Jackson, Gayathri P.S, Dency Davis- Graduate Students of Community Medicine, Pushpagiri Institute of Medical Sciences, Tiruvalla, Kerala, India\u003c/p\u003e\n\u003cp\u003eChikku Babu, Christa Tony M, Deen Stephano Jack \u0026ndash; house surgeons in training of the Community Medicine Department, Pushpagiri Institute of Medical Sciences, Tiruvalla, Kerala, India.\u003c/p\u003e\n\u003cp\u003eReshma V. R. Biostatistician, Community Medicine Department, Pushpagiri Institute of Medical Sciences, Tiruvalla, Kerala, India.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNisha Kurian \u0026ndash; Assistant Professor of Biostatistics, Community Medicine Department, Pushpagiri Institute of Medical Sciences, Tiruvalla, Kerala, India.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnjum John- Assistant Professor of Community Medicine, Pushpagiri Institute of Medical Sciences, Tiruvalla, Kerala, India\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMcCarthy J. What is artificial intelligence? [Internet]. Stanford.edu. [cited 2023 Jul 24]. 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Cureus. 2023;15(9):e44887. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7759/cureus.44887\u003c/span\u003e\u003cspan address=\"10.7759/cureus.44887\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 37814766; PMCID: PMC10560391.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Medical curriculum, Healthcare, Medical ethics, Medical education","lastPublishedDoi":"10.21203/rs.3.rs-3833999/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3833999/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eArtificial Intelligence( AI) is increasingly being integrated into various aspects of human life, including healthcare, with applications such as robotic surgery, virtual nursing assistants, and image analysis. Recognizing the transformative impact of AI in healthcare, the World Medical Association advocates for the inclusion of AI education in medical curricula to prepare healthcare professionals for this emerging field. This study aims to assess medical students' perceptions on AI in medicine, their preferences for structured AI training during medical education, and their understanding of the ethical dimensions associated with AI in healthcare.\u003c/p\u003e\u003ch2\u003eMaterials \u0026amp; Methods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted among 325 medical students in Kerala, India using a pre-validated, semi-structured, self- administered questionnaire. The survey collected demographic information, assessed participants' prior knowledge of AI, and evaluated their self-perceived understanding of AI concepts. Participants' responded to twelve 5-point Likert scale questions regarding their perceptions on AI in medicine and expressed their opinions on the inclusion of certain AI topics in medical curricula.\u003c/p\u003e\u003ch2\u003eResults \u0026amp; Discussion\u003c/h2\u003e \u003cp\u003eMost participants (57.2%) viewed AI as an assistive technology, capable of reducing errors in medical practice. A significant percentage(54.2%) believed that AI could enhance the accuracy of medical decisions, while 48.6% acknowledged its potential to improve patient access to healthcare. Concerns were raised by 37.6% of participants' about the potential decrease in the need for physicians, leading to unemployment. Additionally, apprehensions were expressed regarding the impact of AI on the humanistic aspects of medicine, with 69.2% fearing a decline in the human touch. Participants' also recognized potential challenges to \"trust\"( 52.9%), and the patient- physician relationship(54.5%). Notably, over half of the participants' were uncertain about maintaining professional confidentiality(51.1%) and believed that AI might violate confidentiality( 53.5%). Only 3.7% felt competent enough to inform patients' about features and risks of AI. Participants' expressed a strong need for structured training in AI applications, especially on the topic of \"reducing medical errors\"( 76.9%), and \"ethical issues\" arising from the widespread use of AI in healthcare(79.4%).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study underscores the demand among medical students for structured AI training within the undergraduate medical curriculum, emphasizing the importance of incorporating AI education to meet evolving healthcare needs. While there are widespread ethical concerns, the majority are convinced that AI can be used as an assistive technology in healthcare. The findings contribute essential insights for curriculum development and the definition of learning outcomes in AI education for medical students.\u003c/p\u003e","manuscriptTitle":"Artificial Intelligence in Medical Education- Perception Among Medical Students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-11 16:09:58","doi":"10.21203/rs.3.rs-3833999/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-12T05:38:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-29T17:53:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4336913a-e7a6-458c-8ed8-cc0a4e760c86","date":"2024-02-26T14:48:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15f6acca-1d54-43aa-9764-abd4137a4c24","date":"2024-01-30T10:07:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-01-29T00:32:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2b73925f-9958-4d02-9066-887017e10c94","date":"2024-01-28T09:16:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-18T05:14:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-09T11:50:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-09T11:32:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-09T11:21:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2024-01-04T08:12:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7ed6eae3-1bb7-4378-8b9a-6e6b4af5d3be","owner":[],"postedDate":"January 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-07-29T15:54:26+00:00","versionOfRecord":{"articleIdentity":"rs-3833999","link":"https://doi.org/10.1186/s12909-024-05760-0","journal":{"identity":"bmc-medical-education","isVorOnly":false,"title":"BMC Medical Education"},"publishedOn":"2024-07-27 00:00:00","publishedOnDateReadable":"July 27th, 2024"},"versionCreatedAt":"2024-01-11 16:09:58","video":"","vorDoi":"10.1186/s12909-024-05760-0","vorDoiUrl":"https://doi.org/10.1186/s12909-024-05760-0","workflowStages":[]},"version":"v1","identity":"rs-3833999","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3833999","identity":"rs-3833999","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}