AI in medical education: medical student perception, curriculum recommendations and design suggestions

Navigating the integration of artificial intelligence in the medical education curriculum: a mixed-methods study exploring the perspectives of medical students and faculty in Pakistan

BACKGROUND

The integration of artificial intelligence (AI) into medical education is poised to revolutionize teaching, learning, and clinical practice. However, successful implementation of AI-based tools in medical curricula faces several challenges, particularly in resource-limited settings like Pakistan, where technological and institutional barriers remain significant. This study aimed to evaluate knowledge, attitudes, and practices of medical students and faculty regarding AI in medical education, and explore the perceptions and key barriers regarding strategies for effective AI integration.

METHODS

A concurrent mixed-methods study was conducted over six months (July 2023 to January 2024) at a tertiary care medical college in Pakistan. The quantitative component utilized a cross-sectional design, with 236 participants (153 medical students and 83 faculty members) completing an online survey. Mean composite scores for knowledge, attitudes, and practices were analyzed using non-parametric tests. The qualitative component consisted of three focus group discussions with students and six in-depth interviews with faculty. Thematic analysis was performed to explore participants' perspectives on AI integration.

RESULTS

Majority of participants demonstrated a positive attitude towards AI integration. Faculty had significantly higher mean attitude scores compared to students (3.95 ± 0.63 vs. 3.81 ± 0.75, p = 0.040). However, no statistically significant differences in knowledge (faculty: 3.53 ± 0.66, students: 3.55 ± 0.73, p = 0.870) or practices (faculty: 3.19 ± 0.87, students: 3.23 ± 0.89, p = 0.891) were found. Older students reported greater self-perceived knowledge (p = 0.010) and more positive attitudes (p = 0.016) towards AI, while male students exhibited higher knowledge scores than females (p = 0.025). Qualitative findings revealed key themes, including AI's potential to enhance learning and research, concerns about over-reliance on AI, ethical issues surrounding privacy and confidentiality, and the need for institutional support. Faculty emphasized the importance of training to equip educators with the necessary skills to effectively integrate AI into their teaching.

CONCLUSIONS

This study highlights both the enthusiasm for AI integration and the significant barriers that must be addressed to successfully implement AI in medical education. Addressing technological constraints, providing faculty training, and developing ethical guidelines are critical steps toward fostering the responsible use of AI in medical curricula. These findings underscore the need for context-specific strategies, particularly in resource-limited settings, to ensure that medical students and educators are well-prepared for the future of healthcare.

Foundations of AI for future physicians: A practical, accessible curriculum

WHAT WAS THE EDUCATIONAL CHALLENGE?

The integration of machine learning (ML) and large language models (LLMs) into healthcare is transforming diagnostics, patient care, and administrative workflows. However, most clinicians lack the foundational knowledge to critically engage with these tools, creating risks of overreliance and missed oversight. Just as understanding computed tomography (CT) physics became essential for its safe application, clinicians must acquire basic AI literacy. Practical AI education remains absent from most medical curricula.

WHAT WAS THE SOLUTION?

We propose a modular curriculum using Colab notebooks to teach foundational AI concepts. Colab's free, cloud-based, and interactive environment makes it accessible and engaging, even for non-data scientists. This hands-on approach emphasizes practical applications, enabling learners to explore datasets, build ML models, and interact with locally run LLMs, fostering critical engagement with AI tools.

HOW WAS THE SOLUTION IMPLEMENTED?

The curriculum consists of five interconnected modules: introduction to data science, exploring datasets, predictive modeling, advanced ML techniques and imaging, and working with LLMs. Designed to integrate into medical school data science threads, the curriculum provides structured, progressive learning tailored to clinical contexts.

WHAT LESSONS WERE LEARNED?

Global accessibility, hands-on engagement, and modular design make this approach adaptable across diverse settings. Emphasizing ethical considerations and local relevance enhances its impact.

WHAT ARE THE NEXT STEPS?

The next step is to integrate the Colab notebook-based curriculum into the authors' medical school data science thread. To support broader adoption, adaptable teaching guides will be developed, enabling implementation at other medical schools, including those in low-resource settings, while leveraging Colab's accessibility for regional customization.

Assessing the disconnect between student interest and education in artificial intelligence in medicine in Saudi Arabia

BACKGROUND

Although artificial intelligence (AI) has gained increasing attention for its potential future impact on clinical practice, medical education has struggled to stay ahead of the developing technology. The question of whether medical education is fully preparing trainees to adapt to potential changes from AI technology in clinical practice remains unanswered, and the influence of AI on medical students' career preferences remains unclear. Understanding the gap between students' interest in and knowledge of AI may help inform the medical curriculum structure.

METHODS

A total of 354 medical students were surveyed to investigate their knowledge of, exposure to, and interest in the role of AI in health care. Students were questioned about the anticipated impact of AI on medical specialties and their career preferences.

RESULTS

Most students (65%) were interested in the role of AI in medicine, but only 23% had received formal education in AI based on reliable scientific resources. Despite their interest and willingness to learn, only 20.1% of students reported that their school offered resources enabling them to explore the use of AI in medicine. They relied mainly on informal information sources, including social media, and few students understood fundamental AI concepts or could cite clinically relevant AI research. Students who cited more scientific primary sources (rather than online media) exhibited significantly higher self-reported understanding of AI concepts in the context of medicine. Interestingly, students who had received more exposure to AI courses reported higher levels of skepticism regarding AI and were less eager to learn more about it. Radiology and pathology were perceived to be the fields most strongly affected by AI. Students reported that their overall choice of specialty was not impacted by AI.

CONCLUSION

Formal AI education seems inadequate despite students' enthusiasm concerning the application of such technology in clinical practice. Medical curricula should evolve to promote structured, evidence-based AI literacy to enable students to understand the potential applications of AI in health care.

When Healthcare Professionals Use AI: Exploring Work Well-Being Through Psychological Needs Satisfaction and Job Complexity

This study examines how the use of artificial intelligence (AI) by healthcare professionals affects their work well-being through the satisfaction of basic psychological needs, framed within Self-Determination Theory. Data from 280 healthcare professionals across various departments in Chinese hospitals were collected, and the hierarchical regression and regression were analyzed to assess the relationship between the use of AI, psychological needs satisfaction (autonomy, competence, and relatedness), and their work well-being. The results reveal that the use of AI enhances work well-being indirectly by increasing the satisfaction of these psychological needs. Additionally, job complexity serves as a boundary condition that moderates the relationship between the use of AI and work well-being. Specifically, job complexity weakens the relationship between the use of AI and the satisfaction of autonomy and competence, while having no significant effect on the relationship between the use of AI and the satisfaction of relatedness. These findings suggest that the impact of the use of AI on healthcare professionals' well-being is contingent on job complexity. This study highlights that promoting healthcare professionals' well-being at work in the context of AI adoption requires not only technological implementation but also ongoing adaptation to meet their evolving psychological needs. These insights provide a theoretical foundation and practical guidance for integrating AI into healthcare to support the well-being of healthcare professionals.

Medical students' attitudes toward AI in education: perception, effectiveness, and its credibility

BACKGROUND

The rapid advancement of artificial intelligence (AI) has revolutionized both medical education and healthcare by delivering innovative tools that enhance learning and improve overall outcomes. The study aimed to assess students' perceptions regarding the credibility and effectiveness of AI as a learning tool and to explore the dynamics of integrating AI in medical education.

METHODOLOGY

A cross-sectional study was carried out across medical colleges in Pakistan. A 26-question survey was developed using Google Forms from previously validated studies. The survey assessed demographics of participants, basic understanding of AI, AI as a learning tool in medical education and socio-ethical impacts of the use of AI. The data was analyzed using SPSS (v 26.0) to derive descriptive and inferential statistics.

RESULT

A total of 702 medical students aged 18 to 26 years (mean age 20.50 ± 1.6 years) participated in the study. The findings revealed a generally favorable attitude towards AI among medical students (80.3%), with the majority considering it an effective (60.8%) and credible (58.4%) learning tool in medical education. Students agreed that AI learning optimized their study time (60.3%) and provided up-to-date medical information (63.1%). Notably, 65.7% of students found AI more efficient in helping them grasp medical concepts compared to traditional tools like books and lectures, while 66.8% reported receiving more accurate answers to their medical inquiries through AI. The study highlighted that medical students view traditional tools as becoming increasingly outdated (59%), emphasizing the importance of integrating AI into medical education and creating dedicated AI tools (80%) for the medical education.

CONCLUSION

This study demonstrated that AI is an effective and credible tool in medical education, offering personalized learning experiences and improved educational outcomes. AI tools are helping students learn medical concepts by cutting down on study-time, providing accurate answers, and ultimately improving study outcomes. We recommend developing dedicated AI tools for medical education and their formal integration into medical curricula, along with appropriate regulatory oversight to ensure AI can enhance human abilities rather than acting as a replacement for humans.

S, Pattanayak D, Panda S, Mondal H. Exploring Radiology Postgraduate Students' Engagement with Large Language Models for Educational Purposes: A Study of Knowledge, Attitudes, and Practices

Background  The integration of large language models (LLMs) into medical education has received increasing attention as a potential tool to enhance learning experiences. However, there remains a need to explore radiology postgraduate students' engagement with LLMs and their perceptions of their utility in medical education. Hence, we conducted this study to investigate radiology postgraduate students' knowledge, attitudes, and practices regarding LLMs in medical education. Materials and Methods  A cross-sectional quantitative survey was conducted online on Google Forms. Participants from all over India were recruited via social media platforms and snowball sampling techniques. A previously validated questionnaire was used to assess knowledge, attitude, and practices regarding LLMs. Descriptive statistical analysis was employed to summarize participants' responses. Results  A total of 252 (139 [55.16%] males and 113 [44.84%] females) radiology postgraduate students with a mean age of 28.33 ± 3.32 years participated in the study. The majority of the participants (47.62%) were familiar with LLMs with their potential incorporation with traditional teaching-learning tools (71.82%). They are open to including LLMs as a learning tool (71.03%) and think that it would provide comprehensive medical information (62.7%). Residents take the help of LLMs when they do not get the desired information from books (46.43%) or Internet search engines (59.13%). The overall score of knowledge (3.52 ± 0.58), attitude (3.75 ± 0.51), and practice (3.15 ± 0.57) were statistically significantly different (analysis of variance [ANOVA], p  < 0.0001), with the highest score in attitude and lowest in practice. However, no significant differences were found in the scores for knowledge ( p  = 0.64), attitude ( p  = 0.99), and practice ( p  = 0.25) depending on the year of training. Conclusion  Radiology postgraduate students are familiar with LLM and recognize the potential benefits of LLMs in postgraduate radiology education. Although they have a positive attitude toward the use of LLMs, they are concerned about its limitations and use it only in limited situations for educational purposes.

Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education

Artificial Intelligence (AI) holds immense potential for revolutionizing medical education and healthcare. Despite its proven benefits, the full integration of AI faces hurdles, with ethical concerns standing out as a key obstacle. Thus, educators should be equipped to address the ethical issues that arise and ensure the seamless integration and sustainability of AI-based interventions. This article presents twelve essential tips for addressing the major ethical concerns in the use of AI in medical education. These include emphasizing transparency, addressing bias, validating content, prioritizing data protection, obtaining informed consent, fostering collaboration, training educators, empowering students, regularly monitoring, establishing accountability, adhering to standard guidelines, and forming an ethics committee to address the issues that arise in the implementation of AI. By adhering to these tips, medical educators and other stakeholders can foster a responsible and ethical integration of AI in medical education, ensuring its long-term success and positive impact.

Federated Learning: Breaking Down Barriers in Global Genomic Research

Recent advancements in Next-Generation Sequencing (NGS) technologies have revolutionized genomic research, presenting unprecedented opportunities for personalized medicine and population genetics. However, issues such as data silos, privacy concerns, and regulatory challenges hinder large-scale data integration and collaboration. Federated Learning (FL) has emerged as a transformative solution, enabling decentralized data analysis while preserving privacy and complying with regulations such as the General Data Protection Regulation (GDPR). This review explores the potential use of FL in genomics, detailing its methodology, including local model training, secure aggregation, and iterative improvement. Key challenges, such as heterogeneous data integration and cybersecurity risks, are examined alongside regulations like GDPR. In conclusion, successful implementations of FL in global and national initiatives demonstrate its scalability and role in supporting collaborative research. Finally, we discuss future directions, including AI integration and the necessity of education and training, to fully harness the potential of FL in advancing precision medicine and global health initiatives.

A Survey of Veterinary Student Perceptions on Integrating ChatGPT in Veterinary Education Through AI-Driven Exercises

Artificial intelligence (AI) in education is rapidly gaining attention, particularly with tools like ChatGPT, which have the potential to transform learning experiences. However, the application of such tools in veterinary education remains underexplored. This study aimed to design an AI-driven exercise and investigate veterinary students' perceptions regarding the integration of ChatGPT into their education, specifically within the Year 5 Equine Medicine and Surgery course at City University of Hong Kong. Twenty-two veterinary students participated in an AI-driven exercise, where they created multiple-choice questions (MCQs) and evaluated ChatGPT's responses. The exercise was designed to promote active learning and a deeper understanding of complex concepts. The results indicate a generally positive reception, with 72.7% of students finding the exercise moderately to extremely engaging and 77.3% agreeing that it deepened their understanding. Additionally, 68.2% of students reported improvements in their critical thinking skills. Students with prior AI experience exhibited higher engagement levels and perceived the exercise as more effective. The study also found that engagement positively correlated with perceived usefulness, overall satisfaction, and the likelihood of recommending similar AI-driven exercises in other courses. Qualitative feedback underscored the interactive nature of this exercise and its usefulness in helping students understand complex concepts, although some students experienced confusion with AI-generated responses. While acknowledging the limitations of the technology and the small sample size, this study provides valuable insights into the potential benefits and challenges of incorporating AI-driven tools into veterinary education, highlighting the need for carefully considered integration of such tools into the curriculum.

Developing a Canadian artificial intelligence medical curriculum using a Delphi study

The integration of artificial intelligence (AI) education into medical curricula is critical for preparing future healthcare professionals. This research employed the Delphi method to establish an expert-based AI curriculum for Canadian undergraduate medical students. A panel of 18 experts in health and AI across Canada participated in three rounds of surveys to determine essential AI learning competencies. The study identified key curricular components across ethics, law, theory, application, communication, collaboration, and quality improvement. The findings demonstrate substantial support among medical educators and professionals for the inclusion of comprehensive AI education, with 82 out of 107 curricular competencies being deemed essential to address both clinical and educational priorities. It additionally provides suggestions on methods to integrate these competencies within existing dense medical curricula. The endorsed set of objectives aims to enhance AI literacy and application skills among medical students, equipping them to effectively utilize AI technologies in future healthcare settings.

Attitudes and perceptions of Thai medical students regarding artificial intelligence in radiology and medicine

INTRODUCTION

Artificial Intelligence (AI) has made a profound impact on the medical sector, particularly in radiology. The integration of AI knowledge into medical education is essential to equip future healthcare professionals with the skills needed to effectively leverage these advancements in their practices. Despite its significance, many medical schools have yet to incorporate AI into their curricula. This study aims to assess the attitudes of medical students in Thailand toward AI and its application in radiology, with the objective of better planning for its inclusion.

METHODS

Between February and June 2022, we conducted a survey in two Thai medical schools: Chiang Mai University in Northern Thailand and Prince of Songkla University in Southern Thailand. We employed 5-point Likert scale questions (ranging from strongly agree to strongly disagree) to evaluate students' opinions on three main aspects: (1) their understanding of AI, (2) the inclusion of AI in their medical education, and (3) the potential impact of AI on medicine and radiology.

RESULTS

Our findings revealed that merely 31% of medical students perceived to have a basic understanding of AI. Nevertheless, nearly all students (93.6%) recognized the value of AI training for their careers and strongly advocated for its inclusion in the medical school curriculum. Furthermore, those students who had a better understanding of AI were more likely to believe that AI would revolutionize the field of radiology (p = 0.02), making it more captivating and impactful (p = 0.04).

CONCLUSION

Our study highlights a noticeable gap in the understanding of AI among medical students in Thailand and its practical applications in healthcare. However, the overwhelming consensus among these students is their readiness to embrace the incorporation of AI training into their medical education. This enthusiasm holds the promise of enhancing AI adoption, ultimately leading to an improvement in the standard of healthcare services in Thailand, aligning with the country's healthcare vision.

Acceptance of artificial intelligence in university contexts: A conceptual analysis based on UTAUT2 theory

This systematic review examined, through the UTAUT2 model, the factors influencing the acceptance of artificial intelligence (AI) applications in university contexts. A total of 50 scientific texts published between 2018 and 2023 were analyzed and selected after a rigorous search of specialized databases. These findings confirm the versatility of UTAUT2 in elucidating technological adoption processes in higher education. Performance expectancy and hedonic motivation emerged as significant predictors of intentions and effective use among students, faculty, and administrative staff. Among students, perceived ease of use and social influence were also relevant. The analysis revealed differences in adoption patterns between STEM and non-STEM disciplines and between public and private institutions. Despite widespread positive perceptions of AI's potential, barriers such as distrust and lack of knowledge persist. The research also identified moderating and mediating factors, such as prior technology experience and technological self-efficacy. These results have important implications for the implementation of AI in higher education, suggesting the need for differentiated approaches according to the characteristics of each group and institutional context. It is recommended to develop strategies that address the identified barriers and leverage facilitators, with an emphasis on training, ethical design, and contextual adaptation of AI applications. Future research should explore the longitudinal evolution of these factors and examine AI adoption in non-STEM disciplines in greater depth.

Radiography students' perceptions of artificial intelligence in medical imaging

INTRODUCTION

Education relating to Artificial Intelligence (AI) is becoming critical to developing contemporary radiographers. This study sought to investigate the perceptions of a sample of Australian radiography students regarding AI within the context of medical imaging.

METHODS

Radiography students completed a cross-sectional online questionnaire which obtained quantitative and qualitative data relating to their perceptions and attitudes of AI within the radiographic context. Descriptive and inferential statistics were utilised, and thematic analysis was undertaken for open-text responses.

RESULTS

Responses were gathered from twenty-five participants, in their second, third and fourth year of study. Most participants demonstrated a positive attitude towards AI. Most students view AI to be an assistive tool, though the cohort was less convinced AI would increase future employment in the industry. Females were more likely to disagree that AI will increase work opportunities for the radiographer (p = 0.021), as well as those in their final year of study (p = 0.011). Perceived benefits of AI related to improved work efficiency and image quality. Negative perceptions of AI involved reduced job security, and potential impact on patient care and safety.

DISCUSSION

Students presented a multitude of positive and negative perceptions towards the role that AI may play in their future careers. Education pertaining to AI is central to transforming future clinical practice, and it is encouraging that undergraduate students are intrigued and willing to learn about AI in the radiographic context.

CONCLUSION

This study offers insight into the current perspectives of Australian radiography students on AI within medical imaging, to assist in implementation of future AI-related education in the undergraduate setting.

Comparing the Performance of ChatGPT-4 and Medical Students on MCQs at Varied Levels of Bloom's Taxonomy

Introduction

This research investigated the capabilities of ChatGPT-4 compared to medical students in answering MCQs using the revised Bloom's Taxonomy as a benchmark.

Methods

A cross-sectional study was conducted at The University of the West Indies, Barbados. ChatGPT-4 and medical students were assessed on MCQs from various medical courses using computer-based testing.

Results

The study included 304 MCQs. Students demonstrated good knowledge, with 78% correctly answering at least 90% of the questions. However, ChatGPT-4 achieved a higher overall score (73.7%) compared to students (66.7%). Course type significantly affected ChatGPT-4's performance, but revised Bloom's Taxonomy levels did not. A detailed association check between program levels and Bloom's taxonomy levels for correct answers by ChatGPT-4 showed a highly significant correlation (p<0.001), reflecting a concentration of "remember-level" questions in preclinical and "evaluate-level" questions in clinical courses.

Discussion

The study highlights ChatGPT-4's proficiency in standardized tests but indicates limitations in clinical reasoning and practical skills. This performance discrepancy suggests that the effectiveness of artificial intelligence (AI) varies based on course content.

Conclusion

While ChatGPT-4 shows promise as an educational tool, its role should be supplementary, with strategic integration into medical education to leverage its strengths and address limitations. Further research is needed to explore AI's impact on medical education and student performance across educational levels and courses.

Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine

BACKGROUND

The current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students' attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula.

METHODS

This study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022-2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively.

RESULTS

From a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields.

CONCLUSION

Medical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.