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14 pages, 398 KB

Open AccessArticle

Improving Accuracy in Cardiopulmonary Resuscitation Training: Results on Undergraduate Nursing School Students’ with OMNI2 Simulator

by Fani Alevrogianni, Anna Korompeli, Christos Triantafyllou, Theodoros Katsoulas, Panagiotis Koulouvaris and Pavlos Myrianthefs

Int. Med. Educ. 2025, 4(4), 51; https://doi.org/10.3390/ime4040051 - 25 Nov 2025

Viewed by 305

Abstract

Cardiopulmonary resuscitation (CPR) is a vital skill for healthcare professionals, crucial in life-saving situations. More than 80% of cardiac arrest cases occur out of hospital. As the demand for competent CPR practitioners grows, the effectiveness of training methods becomes increasingly important, especially for [...] Read more.

Cardiopulmonary resuscitation (CPR) is a vital skill for healthcare professionals, crucial in life-saving situations. More than 80% of cardiac arrest cases occur out of hospital. As the demand for competent CPR practitioners grows, the effectiveness of training methods becomes increasingly important, especially for undergraduate students preparing to enter the healthcare field. The primary objective of our study is to investigate the effectiveness of simulation-based teaching methods and by integrating innovative technologies, such as the OMNI2 simulator, to enhance practitioners’ performance and to improve the precision and objectivity of CPR instruction. A cohort of 144 undergraduate students from the Nursing School Department of the National Kapodistrian University of Athens participated in an 8 h Basic Life Support Seminar. It consisted of a 5 h theoretical instruction followed by 3 h of practical training using the OMNI2 simulator. Each student was tasked to identify cardiac arrest and to perform two cycles of CPR according to the 2021 guidelines. Metrics, including total session time, cycles performed, compression-to-ventilation ratio, compression depth, compressions and ventilations per minute, full recoil, peak inspiratory pressure, and ventilation duration, were measured and compared against the simulator’s preset targets. Statistically significant differences (p < 0.05) were observed for all outcomes. In conclusion, while simulation-based teaching has conventionally been proven effective for CPR proficiency, real-time data collected in this study reveal a disparity between anticipated and actual performance. Our research underscores the necessity of refining instructional methods to enhance skill acquisition, potentially leading to improved patient outcomes in the future. Full article

(This article belongs to the Special Issue New Advancements in Medical Education)

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12 pages, 241 KB

Open AccessArticle

Quality Assessment of Pathology Board-Exam-Style MCQs Produced by ChatGPT3.5: A Comparative Study

by Arianna B. Morton, Zunaira Naeem, Allison Goldberg, Alexis Peedin and Joanna Chan

Int. Med. Educ. 2025, 4(4), 49; https://doi.org/10.3390/ime4040049 - 18 Nov 2025

Viewed by 249

Abstract

Residents preparing for pathology board exams frequently use multiple-choice questions (MCQs) from question banks (QBs) like PathDojo and PathPrimer, which can be costly. ChatGPT, a free tool, has been used to generate MCQs for other tests like the SAT. This study compared the [...] Read more.

Residents preparing for pathology board exams frequently use multiple-choice questions (MCQs) from question banks (QBs) like PathDojo and PathPrimer, which can be costly. ChatGPT, a free tool, has been used to generate MCQs for other tests like the SAT. This study compared the quality of pathology MCQs created by ChatGPT versus commercially available study questions for the American Board of Pathology’s (ABPath) certifying exams. A rubric adapted from the National Board of Medical Examiners’ (NBME) question writing guide was validated by two pathologists using commercially available pathology board exam questions. This rubric was then used to evaluate MCQs from commercially available pathology board study books as well as MCQs created by ChatGPT. The results compared the percentage of criteria met between ChatGPT and control MCQs using chi-square analysis with significance set at <0.05. While ChatGPT MCQs were less likely to be accurate compared to commercially available MCQs in four criteria (the best answer choice (82.5% vs. 100%), reflection of current practice (84.6% vs. 100%), error-free explanation (87.9% vs. 100%), and explanation reflecting current practice (87.9% vs. 100%)), the complexity of the ChatGPT-generated questions was higher (78.5% vs. 47.2%). At this time, ChatGPT-generated MCQs should not be used in the same way as commercially available study guides, however there is potential for learned language models (LLM)s to create quality study materials and exam questions with careful monitoring Full article

(This article belongs to the Special Issue New Advancements in Medical Education)

15 pages, 259 KB

Open AccessArticle

Understanding the Role of Large Language Model Virtual Patients in Developing Communication and Clinical Skills in Undergraduate Medical Education

by Urmi Sheth, Margret Lo, Jeffrey McCarthy, Navjeet Baath, Nicole Last, Eddie Guo, Sandra Monteiro and Matthew Sibbald

Int. Med. Educ. 2025, 4(4), 39; https://doi.org/10.3390/ime4040039 - 12 Oct 2025

Viewed by 911

Abstract

Access to practice opportunities for history-taking in undergraduate medical education can be resource-limited. Large language models are a potential avenue to address this. This study sought to characterize changes in learner self-reported confidence with history-taking before and after a simulation with an LLM-based [...] Read more.

Access to practice opportunities for history-taking in undergraduate medical education can be resource-limited. Large language models are a potential avenue to address this. This study sought to characterize changes in learner self-reported confidence with history-taking before and after a simulation with an LLM-based patient and understand learner experience with and the acceptability of virtual LLM-based patients. This was a multi-method study conducted at McMaster University. Simulations were facilitated with the OSCEai tool. Data was collected through surveys with a Likert scale and open-ended questions and semi-structured interviews. A total of 24 participants generated 93 survey responses and 17 interviews. Overall, participants reported a 14.6% increase in comfort with history-taking. Strengths included its flexibility, accessibility, detailed feedback, and ability to provide a judgement-free space to practice. Limitations included its lower fidelity compared to standardized patients and at times repetitive and less clinically relevant feedback as compared to preceptors. It was overall viewed best as a supplement rather than a replacement for standardized patients. In conclusion, LLM-based virtual patients were feasible and valued as an adjunct tool. They can support scalable, personalized practice. Future work is needed to understand objective metrics of improvement and to design curricular strategies for integration. Full article

(This article belongs to the Special Issue New Advancements in Medical Education)

12 pages, 575 KB

Open AccessArticle

Evaluation of Pharmacy and Nursing Interprofessional Undergraduate Learning in a High-Fidelity Simulated Hospital, Supported with a Virtual Online Environment

by Adam P. Forrest, Kyung Min Kirsten Lee, Kevin O’Shaughnessy, Jimit Gandhi and Jacinta L. Johnson

Int. Med. Educ. 2025, 4(4), 38; https://doi.org/10.3390/ime4040038 - 25 Sep 2025

Viewed by 843

Abstract

Pharmacy and nursing professions collaborate closely in healthcare settings. Effective interprofessional practice is now widely recognised as essential for achieving optimal patient care outcomes. Little has been published on nursing-pharmacy Interprofessional learning (IPL) in a simulated environment in Australian contexts. This study aimed [...] Read more.

Pharmacy and nursing professions collaborate closely in healthcare settings. Effective interprofessional practice is now widely recognised as essential for achieving optimal patient care outcomes. Little has been published on nursing-pharmacy Interprofessional learning (IPL) in a simulated environment in Australian contexts. This study aimed to evaluate whether an IPL activity improved participants’ communication confidence, role understanding, clinical knowledge, and preparedness for hospital placement, while also assessing student satisfaction and identifying areas for improvement. A pedagogically structured teaching and learning model was developed, involving a high-fidelity on-campus simulated hospital ward, supplemented with a virtual online environment to immerse nursing and pharmacy students in a realistic clinical environment to achieve deep learning in preparation for safe practice. An online anonymous survey was conducted to evaluate participants’ experience and preparedness following the simulation. 280 students participated and 52 completed the evaluation. Most students reported that the experience boosted their confidence in communicating with other healthcare professionals (82%), increased clinical/therapeutic knowledge (86%), gave them a better understanding of the roles of nurses/pharmacists within the hospital setting (88%) and left them feeling better prepared for hospital placement (85%). Student free-text responses from the evaluation survey further supported the expansion of the IPL sessions in the future. IPL involving nursing and pharmacy students in a simulated hospital builds confidence in communicating and increases self-reported preparedness for placement. Full article

(This article belongs to the Special Issue New Advancements in Medical Education)

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19 pages, 1419 KB

Open AccessArticle

Community-Engaged Learning Within the Medical Curriculum: Evaluating Learning Outcomes and Implementation Challenges

by Rahul Pandit, Rens L. Essers and Helena J. M. Pennings

Int. Med. Educ. 2025, 4(1), 3; https://doi.org/10.3390/ime4010003 - 26 Feb 2025

Cited by 1 | Viewed by 1222

Abstract

Community engaged learning (CEL) is a teaching methodology which aims to bridge the gap between academia and society by collaborating on community-based projects. Inspired by theories of experiential learning and social constructivism, CEL celebrates learning by doing and is a rather novel teaching [...] Read more.

Community engaged learning (CEL) is a teaching methodology which aims to bridge the gap between academia and society by collaborating on community-based projects. Inspired by theories of experiential learning and social constructivism, CEL celebrates learning by doing and is a rather novel teaching methodology within the predominantly theoretical bachelor medical curriculum. Despite CEL’s potential benefits, its implementation faces significant challenges. Here, we investigated how students, accustomed to traditional academic teaching, learn during CEL-infused courses, specifically studying student perception of their learning and identifying the various facilitators and barriers to learning during CEL. The study conducted at Utrecht University’s Faculty of Medicine included second-year medical students participating in a newly introduced CEL course. Using thematic analysis, the study analyzed students’ written reflections collected before and after completion of the course. CEL contributed to developing valuable competencies like empathy, leadership, and communication skills, which go beyond the realm of textbook and classroom-based knowledge. The study further identified key barriers and facilitators, both at personal and organizational levels influencing learning outcome of students. Based on these data, several recommendations have been formulated for all involved parties (students, academic institutions, community partners) which could contribute towards a sustainable embedding of CEL. Full article

(This article belongs to the Special Issue New Advancements in Medical Education)

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20 pages, 2510 KB

Open AccessArticle

Anxiety among Medical Students Regarding Generative Artificial Intelligence Models: A Pilot Descriptive Study

by Malik Sallam, Kholoud Al-Mahzoum, Yousef Meteb Almutairi, Omar Alaqeel, Anan Abu Salami, Zaid Elhab Almutairi, Alhur Najem Alsarraf and Muna Barakat

Int. Med. Educ. 2024, 3(4), 406-425; https://doi.org/10.3390/ime3040031 - 9 Oct 2024

Cited by 7 | Viewed by 6175

Abstract

Despite the potential benefits of generative artificial intelligence (genAI), concerns about its psychological impact on medical students, especially about job displacement, are apparent. This pilot study, conducted in Jordan during July–August 2024, aimed to examine the specific fears, anxieties, mistrust, and ethical concerns [...] Read more.

Despite the potential benefits of generative artificial intelligence (genAI), concerns about its psychological impact on medical students, especially about job displacement, are apparent. This pilot study, conducted in Jordan during July–August 2024, aimed to examine the specific fears, anxieties, mistrust, and ethical concerns medical students harbor towards genAI. Using a cross-sectional survey design, data were collected from 164 medical students studying in Jordan across various academic years, employing a structured self-administered questionnaire with an internally consistent FAME scale—representing Fear, Anxiety, Mistrust, and Ethics—comprising 12 items, with 3 items for each construct. Exploratory and confirmatory factors analyses were conducted to assess the construct validity of the FAME scale. The results indicated variable levels of anxiety towards genAI among the participating medical students: 34.1% reported no anxiety about genAI‘s role in their future careers (n = 56), while 41.5% were slightly anxious (n = 61), 22.0% were somewhat anxious (n = 36), and 2.4% were extremely anxious (n = 4). Among the FAME constructs, Mistrust was the most agreed upon (mean: 12.35 ± 2.78), followed by the Ethics construct (mean: 10.86 ± 2.90), Fear (mean: 9.49 ± 3.53), and Anxiety (mean: 8.91 ± 3.68). Their sex, academic level, and Grade Point Average (GPA) did not significantly affect the students’ perceptions of genAI. However, there was a notable direct association between the students’ general anxiety about genAI and elevated scores on the Fear, Anxiety, and Ethics constructs of the FAME scale. Prior exposure to genAI and its previous use did not significantly modify the scores on the FAME scale. These findings highlight the critical need for refined educational strategies to address the integration of genAI into medical training. The results demonstrate notable anxiety, fear, mistrust, and ethical concerns among medical students regarding the deployment of genAI in healthcare, indicating the necessity of curriculum modifications that focus specifically on these areas. Interventions should be tailored to increase familiarity and competency with genAI, which would alleviate apprehensions and equip future physicians to engage with this inevitable technology effectively. This study also highlights the importance of incorporating ethical discussions into medical courses to address mistrust and concerns about the human-centered aspects of genAI. In conclusion, this study calls for the proactive evolution of medical education to prepare students for new AI-driven healthcare practices to ensure that physicians are well prepared, confident, and ethically informed in their professional interactions with genAI technologies. Full article

(This article belongs to the Special Issue New Advancements in Medical Education)

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8 pages, 1772 KB

Open AccessArticle

Assessment of Postgraduate Academic Productivity Following a Longitudinal Research Program in a Medical School Curriculum

by Hannah Ong, Shaquille Charles, Joshua Ong, Baraa Nawash, Shavin Thomas and John R. Fowler

Int. Med. Educ. 2024, 3(2), 152-159; https://doi.org/10.3390/ime3020013 - 18 Apr 2024

Viewed by 1585

Abstract

Early involvement and exposure to evidence-based research during medical school have been shown to play a formative role in students’ holistic development as future physicians. While there are medical schools encouraging research initiatives, few programs implement 4-year longitudinal research in the curriculum. Here, [...] Read more.

Early involvement and exposure to evidence-based research during medical school have been shown to play a formative role in students’ holistic development as future physicians. While there are medical schools encouraging research initiatives, few programs implement 4-year longitudinal research in the curriculum. Here, the authors categorized graduates as pre-LRP or post-LRP and utilized PubMed’s Advanced Search Builder to identify each graduate’s publications with a time frame that began from 1 year to 7 years post-graduation. The data were then analyzed to identify any significant changes between these two cohorts. A total of 1022 medical school graduates from an ACGME-accredited U.S. medical school were included in this study. The average rate of publications annually was 0.47 + 1.43 (pre-LRP) and 0.57 + 1.40 (post-LRP). Additionally, the average probability of at least one publication in a given year was 22% (95% CI: 0.21–0.23) pre-LRP and 27% (95% CI: 0.25–0.28) post-LRP. Lastly, the average probability of at least one first-author publication in a given year was 12.2% (95% CI: 0.12–0.13) pre-LRP and 15% (95% CI: 0.14–0.16) post-LRP. Overall, participation in a mentored longitudinal research program during medical school demonstrated a positive trend in the number and rate of publications. The implementation of a mentored longitudinal research program can contribute to increased research productivity in physicians’ early careers, leading to the development of important research skills, the fostering of commitment in scholarly work, and a deeper understanding of evidence-based medicine. Full article

(This article belongs to the Special Issue New Advancements in Medical Education)

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Review

Jump to: Research

21 pages, 127827 KB

Open AccessEditor’s ChoiceReview

Artificial Intelligence in Orthopedic Medical Education: A Comprehensive Review of Emerging Technologies and Their Applications

by Kyle Sporn, Rahul Kumar, Phani Paladugu, Joshua Ong, Tejas Sekhar, Swapna Vaja, Tamer Hage, Ethan Waisberg, Chirag Gowda, Ram Jagadeesan, Nasif Zaman and Alireza Tavakkoli

Int. Med. Educ. 2025, 4(2), 14; https://doi.org/10.3390/ime4020014 - 30 Apr 2025

Cited by 4 | Viewed by 3199

Abstract

Integrating artificial intelligence (AI) and mixed reality (MR) into orthopedic education has transformed learning. This review examines AI-powered platforms like Microsoft HoloLens, Apple Vision Pro, and HTC Vive Pro, which enhance anatomical visualization, surgical simulation, and clinical decision-making. These technologies improve the spatial [...] Read more.

Integrating artificial intelligence (AI) and mixed reality (MR) into orthopedic education has transformed learning. This review examines AI-powered platforms like Microsoft HoloLens, Apple Vision Pro, and HTC Vive Pro, which enhance anatomical visualization, surgical simulation, and clinical decision-making. These technologies improve the spatial understanding of musculoskeletal structures, refine procedural skills with haptic feedback, and personalize learning through AI-driven adaptive algorithms. Generative AI tools like ChatGPT further support knowledge retention and provide evidence-based insights on orthopedic topics. AI-enabled platforms and generative AI tools help address challenges in standardizing orthopedic education. However, we still face many barriers that relate to standardizing data, algorithm evaluation, ethics, and the curriculum. AI is used in preoperative planning and predictive analytics in the postoperative period that bridges theory and practice. AI and MR are key to supporting innovation and scalability in orthopedic education. However, technological innovation relies on collaborative partnerships to develop equitable, evidence-informed practices that can be implemented in orthopedic education. For sustained impact, innovation must be aligned with pedagogical theories and principles. We believe that orthopedic medical educators’ future critical role will be to enhance the next generation of competent clinicians. Full article

(This article belongs to the Special Issue New Advancements in Medical Education)

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