Artificial Intelligence in Medical Education: Transformative Potential, Current Applications, and Future Implications.

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) 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.

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.

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.

Artificial Intelligence (AI) has emerged as a transformative force in medical education, offering solutions to the longstanding limitations of traditional training. This article explores AI’s role across five critical domains: clinical training, virtual patient simulation, personalized learning, curriculum integration, and assessment. The COVID-19 pandemic underscored the urgency of flexible learning environments, and AI-based platforms such as Osso VR, Touch Surgery, Body Interact, Smart Sparrow, and Virti illustrate the capacity of intelligent systems to maintain continuity, improve engagement, and benchmark outcomes globally and regionally. In clinical training, AI-powered simulations enhance surgical and procedural practice through immersive, risk-free environments, significantly improving accuracy, retention, and confidence. Virtual patient systems further extend exposure to rare and critical cases, fostering diagnostic reasoning, interprofessional communication, and adaptive decision-making. Personalized learning, enabled by adaptive algorithms, addresses individual needs, ensuring mastery through predictive analytics and targeted remediation. Evidence indicates improved Objective Structured Clinical Examination (OSCE) performance and reduced diagnostic errors when students are guided by AI-supported platforms. Curriculum integration is another key theme: AI modules in radiology, pathology, and internal medicine allow benchmarking against global standards, while simultaneously supporting accreditation requirements. This is particularly salient in Georgia, where medical schools are piloting AI assessment tools to align competencies with international benchmarks. The role of AI in assessment is equally transformative, ranging from automated grading with Natural Language Processing (NLP) to AI-assisted OSCE evaluations and image-based diagnostic testing, ensuring objectivity, scalability, and timeliness. The pandemic context revealed AI’s potential in remote learning, where virtual laboratories, telemedicine training, and AI-based proctoring ensured pedagogical continuity. Research consistently demonstrates that AI-enabled distance learning sustains or enhances retention and student satisfaction compared to traditional modalities. Despite its promise, challenges persist. Ethical issues of data privacy, financial costs, and the necessity of faculty training must be addressed to ensure equitable access. Additionally, cultural adaptation and institutional readiness influence adoption rates, particularly in resource-constrained regions. While the article effectively synthesizes current evidence and real-world applications, it may be strengthened by situating AI within broader educational theory (e.g., constructivist learning) and by comparing outcomes with traditional pedagogies at a meta-analytical level. Future research should focus on cost-effectiveness, faculty–student co-design of AI tools, and long-term impact on patient care outcomes, ensuring that AI serves not only as a technological innovation but as a pedagogically sound transformation of medical education.

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.

To explore the understanding of medical students regarding the integration of AI in medical education. Mixed methods, explanatory sequential study. Place and Duration of the Study: This study was conducted from March to May 2024 at the CMH Medical College, Lahore, Pakistan. A total of 152 undergraduate medical students were recruited. Quantitative surveys were used to measure AI-related attitudes and awareness of the students through a Likert scale, while in-depth insights into challenges and educational impact were obtained through open-ended questions. SPSS version 27 was used for the analysis of quantitative and Nvivo-11 for qualitative data. The study consisted of 152 participants. Most of them 139 (95.9%) had good knowledge about AI and expressed positive views. The majority believed that AI improves medical concepts, patient outcomes, and healthcare delivery, and helps in early disease detection. They agreed that AI will be effective in education 114 (75%) and will have a positive impact on learning experience 111 (73%) and future medical practice 94 (62%), so, it should be mandatory in medical education 90 (59%). Around half of the participants perceived potential job displacement and ethical dilemmas as a challenge due to AI in the future. Major themes emerging from qualitative data were AI-related challenges, topics of interest, and future expectations. The study showed positive views and attitudes towards AI integration in medical education. Participants highlighted various benefits and perceived challenges including ethical concerns and resource limitations. As medical education advances, this subject needs to be studied more for its successful integration into medical education for better results. Understanding, Awareness, Artificial intelligence, Medical education, Medical students.

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.

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.

The study aims to develop, theoretically substantiate, and pilot-test a hybrid educational model designed to foster clear and empathetic medical communication skills in medical students by integrating artificial intelligence tools (virtual patients, speech analysis systems) into the “Russian Language and Culture of Speech” course. The article examines the didactic potential of integrating simulators based on artificial intelligence (AI), virtual patients, and speech analysis systems to overcome the deficit of communicative practice in mass education settings. The authors provide a detailed analysis of the key components of the proposed hybrid model, including the use of adapted virtual patients for practicing typical and complex clinical scenarios within a Russian-speaking context; the application of precision speech and non-verbal behavior analysis systems for objective and instantaneous feedback; and the role of generative analytics and adaptive trajectories in personalizing the learning process. Particular attention is paid to the methodological integration of AI tools into the structure of the “Russian Language and Culture of Speech” course, including modular lesson design, the transformation of the teacher’s role, and ensuring synergy between technological and pedagogical components. The article also considers practical aspects of testing the model at the RUDN University and the Bolashaq Academy, identifying advantages and limitations, as well as ethical, legal, and infrastructural challenges associated with implementing AI in medical education. The scientific novelty lies in the development and theoretical substantiation of a hybrid educational model that ensures synergy between precision AI simulators (virtual patients, speech analysis systems) and the transformed role of the teacher (as a data analyst, mentor, and expert) within a humanities discipline during the early stages of medical education. As a result, a structural-functional hybrid model has been developed and theoretically substantiated. Pilot testing of the model’s elements revealed its didactic potential, evidenced by increased student motivation and engagement, as well as an increase in the number of training attempts. Key conditions and risks for successful implementation were established, including the need to create representative corpora of Russian-language medical dialogues, bridge the digital divide, and comply with ethical and legal norms.

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.

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.

Highlights Review of the potential use of AI in anatomy education, including any weaknesses or threats associated with integrating AI into anatomy teaching. Recommendations for incorporating AI in anatomy education within medical curricula. ABSTRACT Background: Artificial intelligence (AI) refers to machines capable of performing human tasks and exhibiting intelligent behavior. The application of AI in medical education has increased significantly, especially during the COVID-19 pandemic, as remote teaching became widespread across all medical disciplines, including anatomy. Objective: This narrative review aimed to explore the potential use of AI in anatomy education and to identify any associated weaknesses or threats related to its integration. Material and Method: A systematic literature search was conducted using PubMed and Embase (limited to the past 5 years), and Google Scholar (limited to the past 2 years), employing keywords related to "artificial intelligence" and "anatomy education". Inclusion criteria were randomized controlled trials and review articles focusing on AI tools in anatomy education, published in English and freely accessible. Exclusion criteria included abstract-only publications, non-human studies, and studies not specifically related to anatomy education. Of the 3,298 articles identified, seven met the inclusion criteria. Result: Seven articles fulfilled the inclusion criteria. The reviewed literature explored AI applications in anatomy education, highlighting innovations such as blended learning, automated assessments, and digital platforms. Overall, AI demonstrates potential to improve teaching effectiveness and student engagement. However, successful implementation requires strategic planning, adequate infrastructure, and a solid pedagogical framework. Conclusion: AI has emerged as a valuable tool in anatomy education, enhancing learning outcomes, accessibility, and instructional flexibility. Nonetheless, its integration must be carefully managed to overcome current limitations and ensure equitable, effective, and sustainable educational practices.

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