Artificial Intelligence in Medical Education.

Objective: The present research aimed to investigate medical students’ views regarding the use of artificial intelligence (AI) in medical education and to study its impact on their annual examination percentages.Study design and setting: This was a cross-sectional study conducted at Jinnah Medical College, Peshawar, Pakistan.Participants: A total of 200 medical students from various academic years were enrolled in the study using a convenience sampling technique. Students who declined to provide consent were not included in the study.Results: Within the study population, 52 (26%) reported having a high level of knowledge regarding AI, whereas 55 (27.5%) considered their knowledge to be limited. While 166 (83.5%) participants acknowledged awareness of AI applications, only 52 (26%) had received formal education or training in AI during their medical studies. Additionally, 142 (71%) participants believed that AI has the potential to enhance medical education, and 118 (59%) reported prior use of AI tools for study or research purposes.Conclusion: This study highlights that although medical students have a strong awareness of the use of AI, they lack in-depth understanding and formal training. The findings emphasize the need to integrate structured AI education into medical curricula to prepare future healthcare professionals for effective and safe use of AI in academic and clinical practice.

Artificial Intelligence (AI) is increasingly used to support medical students' learning journeys, providing personalized experiences and improved outcomes. We conducted a scoping review to explore the current application and classifications of AI in medical education. Following the PRISMA-P guidelines, we searched four databases, ultimately including 22 studies. Our analysis identified four AI methods used in various medical education domains, with the majority of applications found in training labs. The use of AI in medical education has the potential to improve patient outcomes by equipping healthcare professionals with better skills and knowledge. Post-implementation refers to the outcomes of AI-based training, which showed improved practical skills among medical students. This scoping review highlights the need for further research to explore the effectiveness of AI applications in different aspects of medical education.

Background: Artificial Intelligence (AI), recognized as the fourth industrial revolution, is revolutionizing medical practice through enhanced diagnostics, treatment planning, and healthcare delivery. Its expanded role post-COVID-19 underscores the urgency of AI literacy among future physicians. This study aimed to explore medical students’ perceptions of AI in medical education. Method: A cross-sectional study was conducted from June to July 2024 at CMH and AIMC medical colleges in Pakistan. Using a non-probability convenience sampling technique, an online questionnaire was distributed among MBBS students from both public and private institutions. A minimum of 10% of students from each academic year were invited, yielding a final sample of 139 respondents (response rate: 92%). Associations between categorical variables were analyzed using the chi-square test. Results: Among participants, 57.2% were female and 42.8% male. Most students (96.4%) agreed that computers aid learning, and 93.5% believed AI will be essential in future medical practice. Additionally, 77% felt AI would improve medical education, while 87.7% expressed willingness to receive AI-based instruction. A majority (89.1%) supported the inclusion of AI training in the curriculum, and 83.5% believed AI knowledge would benefit their careers. Female students were significantly more likely to support curriculum integration (p = 0.03) and AI instruction (p = 0.04), though no significant gender differences were observed in perceptions of AI’s future role (p = 0.12). Conclusion: Medical students exhibited highly positive attitudes toward AI. While they do not view AI as replacing physicians, they recognize its transformative role. Integrating AI education into MBBS curricula is therefore essential.

Implementing a reform in medical education requires students’ awareness regarding the importance of artificial intelligence (AI) in modern medical practice. The objective of this study was to investigate students’ perceptions of AI in medical education. A cross-sectional survey was conducted from June 2021 to November 2021 using an online questionnaire to collect data from medical students in the Faculty of Medicine at Kuwait University, Kuwait. The response rate for the survey was 51%, with a sample size of 352. Most students (349 (99.1%)) agreed that AI would play an important role in healthcare. More than half of the students (213 (60.5%)) understood the basic principles of AI, and (329 (93.4%)) students showed comfort with AI terminology. Many students (329 (83.5%)) believed that learning about AI would benefit their careers, and (289 (82.1%)) believed that medical students should receive AI teaching or training. The study revealed that most students had positive perceptions of AI. Undoubtedly, the role of AI in the future of medicine will be significant, and AI-based medical practice is required. There was a strong consensus that AI will not replace doctors but will drastically transform healthcare practices.

Artificial intelligence (AI) has the potential to transform medical training through adaptive learning, immersive simulations, automated assessments, and data-driven insights, offering solutions to persistent issues such as high student-to-faculty ratios, overcrowded classrooms, and limited clinical exposure. Globally, many universities have already embedded AI literacy and competencies into undergraduate, postgraduate, and continuing education programs, while in Vietnam, the use of AI in medical education remains limited and fragmented. Most students have little formal exposure to AI, and empirical evidence on faculty or institutional readiness is scarce. Experiences from other countries, including Malaysia, Palestine, and Oman, demonstrate that incremental adoption and faculty development can facilitate cultural acceptance and curricular innovation, providing useful lessons for Vietnam. At the same time, significant barriers remain. These include inadequate infrastructure in provincial universities, low levels of AI literacy among both students and educators, underdeveloped regulatory and ethical frameworks, and resistance to pedagogical change. Cost-effectiveness and sustainability are additional concerns in a middle-income context, where upfront investments must be balanced against long-term benefits and equitable access. Advancing AI in Vietnamese medical education will therefore require a coordinated national strategy that prioritizes infrastructure, AI literacy, faculty development, quality assurance, and sustainable funding models, alongside ethical and legal safeguards. By addressing these key foundations, Vietnam can harness AI not only to modernize medical education but also to strengthen preparedness for a digitally enabled health workforce.

BackgroundLarge language models (LLMs) have demonstrated significant potential in diverse domains, including medicine. Nonetheless, there is a scarcity of studies examining their performance in medical examinations, especially those conducted in languages other than English, and in direct comparison with medical students. Analyzing the performance of LLMs in state medical examinations can provide insights into their capabilities and limitations and evaluate their potential role in medical education and examination preparation. ObjectiveThis study aimed to assess and compare the performance of 3 LLMs, GPT-4, Bing, and GPT-3.5-Turbo, in the German Medical State Examinations of 2022 and to evaluate their performance relative to that of medical students. MethodsThe LLMs were assessed on a total of 630 questions from the spring and fall German Medical State Examinations of 2022. The performance was evaluated with and without media-related questions. Statistical analyses included 1-way ANOVA and independent samples t tests for pairwise comparisons. The relative strength of the LLMs in comparison with that of the students was also evaluated. ResultsGPT-4 achieved the highest overall performance, correctly answering 88.1% of questions, closely followed by Bing (86.0%) and GPT-3.5-Turbo (65.7%). The students had an average correct answer rate of 74.6%. Both GPT-4 and Bing significantly outperformed the students in both examinations. When media questions were excluded, Bing achieved the highest performance of 90.7%, closely followed by GPT-4 (90.4%), while GPT-3.5-Turbo lagged (68.2%). There was a significant decline in the performance of GPT-4 and Bing in the fall 2022 examination, which was attributed to a higher proportion of media-related questions and a potential increase in question difficulty. ConclusionsLLMs, particularly GPT-4 and Bing, demonstrate potential as valuable tools in medical education and for pretesting examination questions. Their high performance, even relative to that of medical students, indicates promising avenues for further development and integration into the educational and clinical landscape.

Artificial intelligence (AI) models, like Chat Generative Pre-Trained Transformer (OpenAI, San Francisco, CA), have recently gained significant popularity due to their ability to make autonomous decisions and engage in complex interactions. To fully harness the potential of these learning machines, users must understand their strengths and limitations. As AI tools become increasingly prevalent in our daily lives, it is essential to explore how this technology has been used so far in healthcare and medical education, as well as the areas of medicine where it can be applied. This paper systematically reviews the published literature on the PubMed database from its inception up to June 6, 2024, focusing on studies that used AI at some level in medical education, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Several papers identified where AI was used to generate medical exam questions, produce clinical scripts for diseases, improve the diagnostic and clinical skills of students and clinicians, serve as a learning aid, and automate analysis tasks such as screening residency applications. AI shows promise at various levels and in different areas of medical education, and our paper highlights some of these areas. This review also emphasizes the importance of educators and students understanding AI's principles, capabilities, and limitations before integration. In conclusion, AI has potential in medical education, but more research needs to be done to fully explore additional areas of applications, address the current gaps in knowledge, and its future potential in training healthcare professionals.

Artificial intelligence (AI) is increasingly influencing medical education by enabling adaptive learning, AI-assisted assessment, and scalable instructional tools. Natural language processing, machine learning, and generative large language models offer innovative ways to support teaching and learning, yet their integration raises ethical, pedagogical, and infrastructural challenges. This viewpoint article aims to examine the current applications, benefits, and challenges of AI in medical education and propose strategies for responsible and effective integration. AI tools such as chatbots, virtual patients, and intelligent tutoring systems enhance personalized and immersive learning. Automated grading and predictive analytics support efficient evaluations, while AI-assisted writing tools streamline content creation. Despite these advances, concerns persist around data privacy, algorithmic bias, unequal access, and diminished critical thinking. Key solutions include AI literacy training, data oversight, equitable infrastructure, and curriculum reform. The FACETS framework offers 6 dimensions (ie, form, application, context, instructional mode, technology, and the SAMR [substitution, augmentation, modification, redefinition model]) to evaluate AI integration effectively. AI offers substantial opportunities to transform medical education, but its adoption must be ethical, equitable, and pedagogically grounded. Strategic frameworks such as FACETS, combined with institutional governance and cross-sector collaboration, are essential to guide implementation so that AI enhances learning outcomes while preserving the humanistic foundations of medical practice.

As artificial intelligence (AI) technologies advance and become increasingly integrated into medicine and healthcare services, there is a growing consensus that it is necessary to prepare medical students to understand and utilize AI in medical education. Research and discussions are ongoing regarding the AI competencies that healthcare professionals should have. There are diverse opinions on how to integrate the necessary AI competencies for medical graduates into existing curricula. However, wide agreement exists regarding the importance of providing sufficient and appropriate education on the ethical aspects of using AI in clinical practice and research. In this paper, the authors aim to introduce practical educational principles, strategies, and methods for educators interested in teaching AI ethics in medicine. To achieve this, the paper (1) introduces the AI competencies and medical ethics competencies that medical school graduates should possess; (2) explains the necessity of fostering AI ethics competencies in medical education; (3) discusses the principles of developing AI ethics education and considerations for implementing such curricula; and (4) presents educational modules that can be utilized to cultivate AI ethics competencies in medical students and young physicians. The authors of the paper hope that the case-based module on medical AI ethics we have developed may contribute to prepare medical graduates who are familiar with the principles and guidelines of medical AI ethics, enabling them to make the best ethical choices in any given environment.

Background: The rapid integration of artificial intelligence (AI) into medical education in developing nations necessitates that educators develop comprehensive AI competencies and readiness. This study explores AI competence and readiness among medical educators in higher education, focusing on the five key dimensions of the ADELE technique: (A) AI Awareness, (D) Development of AI Skills, (E) AI Efficacy, (L) Leanings Towards AI, and (E) AI Enforcement. Structured surveys were used to assess AI competencies and readiness among medical educators for the sustainable integration of AI in medical education. Methods: A cross-sectional study was conducted using a 40-item survey distributed to 253 educators from the Middle East (Saudi Arabia, Egypt, Jordan) and South Asia (India, Pakistan, Philippines). Statistical analyses examined variations in AI competency and readiness by gender and nationality and assessed their predictive impact on the adoption of sustainable AI in medical education. Results: The findings revealed that AI competency and readiness are the primary drivers of sustainable AI adoption, highlighting the need to bridge the gap between theoretical knowledge and practical application. No significant differences were observed based on gender or discipline, suggesting a balanced approach to AI education. However, ethical perspectives on AI integration varied between Middle East and South Asian educators, likely reflecting cultural influences. Conclusions: This study underscores the importance of advancing from foundational AI knowledge to hands-on applications while promoting responsible AI use. The ADELE technique provides a strategic approach to enhancing AI competency in medical education within developing nations, fostering both technological proficiency and ethical awareness among educators.

Introduction: Artificial intelligence (AI) is revolutionizing medical education by enhancing learning experiences, improving knowledge retention, and providing personalized guidance. Through interactive simulations, virtual tutors, and adaptive learning platforms, AI-powered solutions help students in preclinical education grasp difficult ideas. Thus this study aims to examine the impact of AI on preclinical medical education students' academic performance, engagement, and readiness for clinical training among preclinical medical students, examining its applicability, efficacy, potential, and limitations. Which is done by outlining the objectives and evaluating the effectiveness, opportunities, and challenges of integrating artificial intelligence (AI) tools in medical education among Year 1 and Year 2 MBBS students in a private medical university, Malaysia. Methodology: The study involved 300 sample population, including 152 Year 1 and 148 Year 2 students. A cross-sectional questionnaire-based study was conducted, with a sample size of 184 with a 95% confidence level. Data were collected through online surveys and analysis using Microsoft Excel and IBM SPSS version 23.0.Result: All 184 preclinical students used AI tools in their medical education, mainly relying on ChatGPT. About 84.2% are familiar with AI in this context. The effectiveness of AI in improving learning was rated from 1 to 3, with most students scoring AI as 4 or 5 in problem-solving, decision-making, critical thinking, and inspiring new ideas, indicating a high perception of its effectiveness. Many believe AI supports traditional teaching. However, concerns exist about over-reliance on technology (83.2%) and loss of critical thinking skills (77.7%). Also, 42.9% rated their worries about AI's impact on clinical decision-making skills as a 3. Conclusion: Most preclinical students know about AI in medical education and believe it helps improve learning. AI assists students in solving problems, making decisions, encouraging critical thinking, and generating new ideas. However, concerns felt about much dependence on technology and weaken critical thinking skills in medical education. Students believe that AI will not entirely harm clinical decision-making skills. In summary, AI offers both advantages and disadvantages in medical education.

This article was migrated. The article was marked as recommended. As Artificial Intelligence (AI) develops in medicine, there is a greater awareness that medical education may also benefit from AI. Many AI projects are already underway, and many more are still to come. Most medical education administrators and educators are aware of AI, but are not necessarily familiar enough with it to understand the areas of possible application in both using AI in medical education, and also in the content areas that need to be raised with their students. As such, they are at a disadvantage of not understanding the current lie of the land, and are at an even greater disadvantage of not being able to influence and guide future AI projects. This paper attempts to provide an AI conceptual and practical framework for medical education administrators and educators, so that they may have a clearer understanding of the current situation, and may be better placed to guide future AI developments to meet their needs in medical education.

Generative AI (GenAI) is a disruptive technology likely to generate a major impact on faculty and learners in medical education. This work aims to measure the perception of GenAI among medical educators and to gain insights into its major advantages and concerns in medical education. A survey invitation was distributed to medical education faculty of colleges of allopathic and osteopathic medicine within a single university during the fall of 2023. The survey comprised 12 items, among those assessing the role of GenAI for students and educators, the need to modify teaching approaches, GenAI's perceived advantages, applications of GenAI in the educational context, and the concerns, challenges, and trustworthiness associated with GenAI. Responses were obtained from 48 faculty. They showed a positive attitude toward GenAI and disagreed on GenAI having a very negative effect on either the students' or faculty's educational experience. Eighty-five percent of our medical schools' faculty responded to had heard about GenAI, while 42% had not used it at all. Generating text (33%), automating repetitive tasks (19%), and creating multimedia content (17%) were some of the common utilizations of GenAI by school faculty. The majority agreed that GenAI is likely to change its role as an educator. A perceived advantage of GenAI in conducting more effective background research was reported by 54% of faculty. The greatest perceived strengths of GenAI were the ability to conduct more efficient research, task automation, and increased content accessibility. The faculty's major concerns were cheating in home assignments in assessment (97%), tendency for blunder and false information (95%), lack of context (86%), and removal of human interaction in important feedback processes (83%). The majority of the faculty agrees on the lack of guidelines for safe use of GenAI from both a governmental and an institutional policy. The main perceived challenges were cheating, the tendency of GenAI to make errors, and privacy concerns.The faculty recognized the potential impact of GenAI in medical education. Careful deliberation of the pros and cons of GenAI is needed for its effective integration into medical education. There is general agreement that plagiarism and lack of regulations are two major areas of concern. Consensus-based guidelines at the institutional and/or national level need to start to be implemented to govern the appropriate use of GenAI while maintaining ethics and transparency. Faculty responses reflect an optimistic and favorable outlook on GenAI's impact on student learning.

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To the Editor: I read with great interest the commentary by Carin, 1 which makes several thought-provoking points on the application of artificial intelligence (AI) and deep learning (DL) within medical education. As a clinical medical student with a background in data science and AI, I would like to offer a perspective on the integration of deep learning into undergraduate medical curricula. Carin states that future clinicians should learn to understand the fundamentals of DL, appreciate opportunities for its use, and recognize its limitations. This poses a question: When and how should these skills be taught? In ushering in a new era of AI-literate clinicians, it would be important to offer medical students teaching in the relevance and applications of DL and other novel technologies from the beginning of their clinical education. Early exposure would not only allow learners to communicate the uses and limitations of these technologies effectively to colleagues but also offer them the expertise and confidence to advocate for their use in research, teaching, and clinical practice from the beginning of their careers. As an example, my institution in London offers a novel intercalated degree in mathematics and computing targeted at third-year medical students. 2 By teaching skills in programming and AI, the course seeks to produce physicians who will drive forward the process of adopting these technologies in both clinical and research settings. During the 1-year program, students are offered basic training in pure mathematics, programming, mathematical modeling and AI theory before being given the opportunity to undertake supervised biomedical research projects incorporating AI and DL. The course has been well received by students, suggesting that similar curricula could be useful in developing awareness of these potentially transformative technologies. Even a basic understanding of AI may help to alleviate the “black box” effect, a recognized obstacle to its widespread adoption in medicine. 3 Similar programs may also help address the growing regulatory 4 challenges of implementing AI in medicine by offering students skills in critically appraising interventions based on these technologies. DL and AI have significant potential to transform medical education and clinical practice. As these technologies gain traction in clinical practice and pedagogy, it may become necessary for medical educators to find space for them in undergraduate curricula. However, significant attention should be given to when, how, and why these skills are introduced to students.