Why medical education #mededu Without Artificial Intelligence Still Matters: A Neuroscience-Informed Perspective In their recent viewpoint, Izquierdo-Condoy et al. (2026) describe the transformative potential of artificial intelligence (AI) in medical education #mededu and advocate for integrating AI literacy into medical curricula. While supporting this perspective, this Letter highlights a complementary and underexplored issue: the educational implications of clinical practice in contexts where AI support is unavailable or withdrawn after AI-mediated training. Emerging neurophysiological and behavioral evidence suggests that sustained reliance on generative AI may reduce distributed neural engagement during cognitive tasks and alter performance patterns when AI assistance is removed. Additional concerns from medical education #mededu literature indicate that substituting AI for clinical reasoning may affect skill acquisition and retention. These potential vulnerabilities may become particularly salient in high-stakes, resource-constrained environments where independent cognitive performance is critical. Ensuring safe and equitable care may therefore require deliberate preservation of AI-independent competence alongside AI integration. Structured AI-withdrawal exercises, defined thresholds of unaided proficiency, and longitudinal neurocognitive research are proposed to better understand and mitigate possible unintended consequences of AI-mediated learning. Preparing future physicians to practice both with and without AI support may represent an important safeguard for patient safety and professional autonomy.

New in JMIR MedEdu: Why medical education #mededu Without Artificial Intelligence Still Matters: A Neuroscience-Informed Perspective

Authors’ Reply: Why Medical Education Without Artificial Intelligence Still Matters: A Neuroscience-Informed Perspective

New in JMIR MedEdu: Authors’ Reply: Why medical education #mededu Without Artificial Intelligence Still Matters: A Neuroscience-Informed Perspective

ChatGPT versus UpToDate in Preclinical medical education #mededu: Cross-Sectional Analysis Using Term Frequency–Inverse Document Frequency Cosine Similarity Background: Generative artificial intelligence tools such as ChatGPT are increasingly used by medical students for self-directed learning. Although these models demonstrate linguistic fluency, their reliability as supplementary resources for preclinical education remains uncertain. In particular, comparisons with evidence-based references such as UpToDate are lacking. Objective: This study evaluated the similarity between responses generated by ChatGPT (with GPT-4o mini) and those from UpToDate to preclinical medical education #mededu questions to assess ChatGPT’s potential as an adjunctive learning tool. Methods: We conducted a cross-sectional comparison study using 150 first-order questions derived from a preclinical question bank at a single allopathic institution under the oversight of a medical educator with more than 25 years of teaching experience. Each question was entered into ChatGPT 10 times in separate chat sessions, and responses from UpToDate were retrieved from the most relevant articles. The responses were preprocessed through lemmatization, stop-word removal, punctuation removal, and numeric normalization. Similarity between ChatGPT and UpToDate responses was quantified using term frequency–inverse document frequency (TF-IDF) cosine similarity. To determine whether the observed similarities exceeded chance, ChatGPT outputs were compared with a null distribution generated from randomized text. Results: ChatGPT responses demonstrated statistically significant similarity to UpToDate in 59.3% (89/150) of questions. Across subject areas, pharmacology showed the highest concordance (mean cosine similarity 0.338, SD 0.134), followed by pathology (mean 0.321, SD 0.142), biochemistry (mean 0.296, SD 0.120), microbiology (mean 0.297, SD 0.108), and immunology (mean 0.275, SD 0.102). All subject-level similarity scores exceeded those generated from randomized text, confirming that the observed overlap was nonrandom. Conclusions: ChatGPT with GPT-4o mini exhibited moderate but meaningful alignment with UpToDate across preclinical topics, performing best in fact-based disciplines such as pharmacology. Although it is not a substitute for evidence-based resources, ChatGPT may serve as an accessible adjunctive tool for medical students. Integration into preclinical learning should be coupled with artificial intelligence literacy training to promote responsible use and critical appraisal.

New in JMIR MedEdu: ChatGPT versus UpToDate in Preclinical medical education #mededu: Cross-Sectional Analysis Using Term Frequency–Inverse Document Frequency Cosine Similarity

Learning Analytics of a National Entrustable Professional Activities Platform: System-Level Constraints on Advanced Entrustment in Competency-Based ##MedicalEducation #mededu Date Submitted: Mar 13, 2026. Open Peer Review Period: Mar 16, 2026 - May 11, 2026.

Reminder>> Learning Analytics of a National Entrustable Professional Activities Platform: System-Level Constraints on Advanced Entrustment in Competency-Based ##MedicalEducation #mededu (preprint) #openscience #PeerReviewMe #PlanP

Dopamine, Distraction, and Disruption: Perspectives on How Technology and Generation Z Are Reshaping medical education #mededu This viewpoint reflects on how Generation Z (born between 1995 and 2009), shaped by constant digital engagement, a growing awareness of mental health, and a dopamine-driven environment, is transforming medical education #mededu and practice. We explore, from a reflective and interdisciplinary perspective, how the defining characteristics of Generation Z, such as their familiarity with technology, demand for emotional safety, and resistance to traditional hierarchies, might reshape the ways we teach, learn, and practice medicine. Drawing on neuroscience, psychology, sociology, and the medical education #mededu literature, this viewpoint emphasizes the need to move beyond knowledge transmission and foster self-regulation, critical thinking, and ethical judgment. We call for a deliberate and compassionate adaptation of medical education #mededu to cultivate the skills required for a profession increasingly practiced in a context of overstimulation and complexity.

New in JMIR MedEdu: Dopamine, Distraction, and Disruption: Perspectives on How Technology and Generation Z Are Reshaping medical education #mededu

Learning Analytics of a National Entrustable Professional Activities Platform: System-Level Constraints on Advanced Entrustment in Competency-Based ##MedicalEducation #mededu Date Submitted: Mar 13, 2026. Open Peer Review Period: Mar 16, 2026 - May 11, 2026.

Learning Analytics of a National Entrustable Professional Activities Platform: System-Level Constraints on Advanced Entrustment in Competency-Based ##MedicalEducation #mededu (preprint) #openscience #PeerReviewMe #PlanP

"Strengths, Limitations, and Distinct Characteristics of VR and Actor-Based Simulation in ##MedicalEducation #mededu: #Protocol for a Scoping Review" Date Submitted: Mar 4, 2026. Open Peer Review Period: Mar 6, 2026 - May 1, 2026.

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Evaluating Microlearning for Faculty Development in medical education #mededu: Mixed Methods Pilot Study This mixed methods pilot study evaluates the feasibility and effectiveness of microlearning for faculty development in cardiovascular education. Microlearning appears feasible and well-received for faculty development, offering a scalable, flexible approach.

New in JMIR MedEdu: Evaluating Microlearning for Faculty Development in medical education #mededu: Mixed Methods Pilot Study

"Strengths, Limitations, and Distinct Characteristics of VR and Actor-Based Simulation in ##MedicalEducation #mededu: #Protocol for a Scoping Review" Date Submitted: Mar 4, 2026. Open Peer Review Period: Mar 6, 2026 - May 1, 2026.

"Strengths, Limitations, and Distinct Characteristics of VR and Actor-Based Simulation in ##MedicalEducation #mededu: #Protocol for a Scoping Review" (preprint) #openscience #PeerReviewMe #PlanP

Perceptions and Attitudes of Medical Students Toward the Integration of Large Language Models in medical education #mededu: Cross-Sectional Survey in China Background: Although artificial intelligence (AI) is being rapidly integrated into medical education #mededu, insights from medical students, particularly in the Chinese context, remain limited. Objective: This study was designed to explore Chinese medical students’ perceptions of and attitudes toward the integration of AI into medical education #mededu, as well as the factors that may influence their perspectives. The findings of this research offer valuable insights to assist medical educators in the effective implementation of these innovative educational approaches. Methods: On the basis of the estimated total number of clinical medical students at the target institutions, the sample size was calculated to be 379. A web-based questionnaire survey was designed to investigate the acceptance level of medical students toward the application of AI. The questionnaire consisted of 14 questions across 4 dimensions, which included demographic characteristics, perceptions of AI application, willingness, and concerns. Each dimension contained 3 to 4 questions. Descriptive statistics were used to tabulate the frequency of each variable. Chi-square tests and multiple regression analyses were conducted to measure the influencing factors. Results: A total of 566 cross-sectional online surveys were distributed from December 2023 to January 2024 through a snowball sampling method. Finally, 490 medical students from various local tertiary medical centers were involved. Overall, a majority of the participants showed a positive attitude toward future learning and the usage of AI, manifested as totally willing to acquire relevant knowledge (222/490, 45.3%), totally willing to use AI tools (230/490, 46.9%), and totally desiring that schools would offer AI-related courses (230/490, 46.9%). However, there is still a large proportion (392/490, 80.0%) of participants who expressed concerns regarding ethical issues. The findings also indicated that gender and educational level were significantly correlated with the AI application. Specifically, regression analysis indicated that male participants were more inclined to acquire AI information through social media (odds ratio 0.458, 95% CI 0.33‐0.67;

New in JMIR MedEdu: Perceptions and Attitudes of Medical Students Toward the Integration of Large Language Models in medical education #mededu: Cross-Sectional Survey in China

Application of AI-Generated Content in medical education #mededu: Systematic Review of the Impact on Critical Thinking Abilities of Medical Students Background: With the rapid development of artificial intelligence technology, artificial intelligence–generated content (AIGC) is increasingly widely applied in the field of medical education #mededu. Large language models, such as ChatGPT, are a prominent type of AIGC technology. Critical thinking is a core ability in medical education #mededu, but the impact of AIGC technology on the critical thinking ability of medical students remains unclear. Medical students are at a crucial stage in cultivating critical thinking, and the intervention of AIGC technology may have a profound impact on this process. Objective: This study aims to systematically review the impact of AIGC technology on the complex mechanisms affecting medical students’ critical thinking abilities and build a corresponding strategic framework. The findings are intended to provide theoretical support and practical guidance for applying AIGC in medical education #mededu. Methods: This study followed 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, with the retrieval scope limited to English studies published between November 2022 and June 2025. Through the PubMed database, combined with the search methods of subject terms and free words, relevant studies involving the impact of AIGC on the critical thinking of medical students were screened for using keywords such as “AIGC,” “medical students,” and “critical thinking.” Two independent reviewers screened and evaluated the literature, and ultimately conducted a qualitative analysis based on the common themes extracted from the literature. Results: AIGC technology in medical education #mededu is 2-fold. First, AIGC’s powerful information capabilities provide abundant learning resources and efficient tools. This accelerates knowledge acquisition and broadens learning scope. Second, overreliance on AIGC may lead to mental inertia, weaken critical thinking skills, and cause academic integrity issues among students. Research has found that strategies such as customized AIGC tools, virtual standardized patients, new models of resource integration, and proactive assessment of AI limitations can effectively make up for the deficiencies of AIGC in cultivating high-level critical thinking, helping medical students maintain and enhance their critical thinking and problem-solving abilities. Conclusions: AIGC technology application in medical education #mededu needs to carefully weigh the pros and cons. By optimizing the design and usage of AIGC tools and combining them with the guidance and supervision of educators, they can be transformed into powerful tools for promoting the development of critical thinking among medical students. Future research should further expand the scope of study, optimize research methods, pay attention to individual differences, track long-term effects, and deeply explore the influence of ethical and cultural factors to more comprehensively assess the application potential and challenges of AIGC technology in medical education #mededu.

New in JMIR MedEdu: Application of AI-Generated Content in medical education #mededu: Systematic Review of the Impact on Critical Thinking Abilities of Medical Students

The Design and Evaluation of an Online Continuing medical education #mededu App for Medical Professionals in China: Quantitative Study Background: As an emerging delivery mode of education, online continuing medical education #mededu (CME) increases the accessibility of high-quality medical training for professionals and students in China. Guoyuan (meaning “nationwide” in Chinese) is an online CME platform delivered via a mobile app and operated by the National Telemedicine Center of China since 2018, serving as an illustrative case of mobile online CME implementation. Objective: We identified trends in the adoption and usage of the Guoyuan mobile online CME platform from 2018 to 2023 and provided evidence for the application and optimization of online CME. Methods: We analyzed yearly usage data of the Guoyuan mobile app (The First Affiliated Hospital of Zhengzhou University) in 2018-2023 and collected surveys on the satisfaction and recognition of competency enhancement in online CME in each connected hospital in 2023. Using the IBM SPSS, the nonparametric Kruskal-Wallis test was used to compare attendance across different disciplines, followed by post hoc pairwise comparisons for course types with significant differences and ordinal logistic regression analysis to examine factors influencing satisfaction with the online CME system and perceived competency enhancement among invited doctors. Results: From 2018 to 2023, Guoyuan had 94,537 registered trainees, 1672 published course videos, and 1,878,437 attendances. Attendance was higher for courses in ophthalmology, otolaryngology, and pathology than in other disciplines (median attendance 610, IQR 105-2055 vs 283, IQR 106-690 participants). Based on a sample size of 245 participants, ordinal regression analysis showed that discipline category, professional title, and working years significantly influenced satisfaction. General practitioners showed lower overall satisfaction than internal medicine doctors (odds ratio [OR] 0.323, 95% CI 0.110-0.948; OR 0.251, 95% CI 0.087-0.729; and OR 0.196, 95% CI 0.066-0.585; =.04; =.01=.003). Junior titles reported higher audio-visual clarity (OR 3.151, 95% CI 1.178-8.427; .02) and process satisfaction (OR 4.939, 95% CI 1.674-14.576; =.004). More experienced doctors had higher system usability (OR 1.102, 95% CI 1.012-1.200; =.03) and process satisfaction (OR 1.141, 95% CI 1.044-1.247; =.003). Recognition of online CME’s benefits was influenced by multiple factors. Greater clinical experience positively predicted recognition of clinical use (OR 1.106, 95% CI 1.004-1.218; =.04), while an inverse association was observed with age (OR 0.894, 95% CI 0.802-0.996; =.04). For research-related benefits, positive predictors included discipline category in obstetrics and gynecology compared to internal medicine (OR 6.217, 95% CI 1.236-31.258; =.03) and junior professional title (OR 3.791, 95% CI 1.231-11.673; =.02), whereas intensive care unit was a negative predictor compared to internal medicine (OR 0.111, 95% CI 0.014-0.893; =.04). Conclusions: Online mobile CME platforms have gained widespread adoption among medical professionals in China, particularly after the #covid19 outbreak. However, substantial disciplinary disparities in course availability and user experience persist, indicating the need for further optimization of course design and software interaction.

New in JMIR MedEdu: The Design and Evaluation of an Online Continuing medical education #mededu App for Medical Professionals in China: Quantitative Study

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Artificial Intelligence in medical education #mededu: Transformative Potential, Current Applications, and Future Implications Artificial intelligence (AI) is revolutionizing medical education #mededu by enabling personalized, interactive, and efficient learning experiences. Powered by technologies such as natural language processing, machine learning, and generative models, AI supports a wide range of educational tasks from literature synthesis and virtual simulations to curriculum design, automated assessments, and academic writing. These innovations enhance clinical reasoning, streamline administrative processes, and optimize learner feedback across diverse educational settings. However, the integration of AI also presents critical challenges, including ethical concerns, data privacy risks, algorithmic bias, and unequal access in low-resource environments. Ensuring the responsible and equitable use of AI in medical education #mededu requires robust digital literacy, high-quality data, and collaborative regulatory frameworks. By embracing interdisciplinary collaboration and ethical integration, AI holds the potential to advance medical training while preserving humanistic values and improving global health education outcomes.

New in JMIR MedEdu: Artificial Intelligence in medical education #mededu: Transformative Potential, Current Applications, and Future Implications

Digital Choice Architecture in medical education #mededu: Applying Behavioral Economics to Online Learning Environments Healthcare has widely adopted behavioral economics to influence clinical practice, with documented success using defaults and social comparison feedback in electronic health records. Yet online medical education #mededu, now the dominant modality for continuing professional development, remains designed on assumptions of rational learning that behavioral science has disproven in clinical contexts. This viewpoint examines the paradox of applying sophisticated behavioral insights to clinical work while designing digital learning environments as if learners are immune to cognitive limitations. We propose digital choice architecture for medical education #mededu: intentional integration of behavioral design principles into learning management systems and online platforms. Drawing from clinical nudge units and implementation science, we demonstrate how defaults, social norms, and commitment devices can be systematically applied to digital continuing education. As medical education #mededu becomes increasingly technology-mediated, behavioral science provides theoretical foundation and practical tools for designing online learning environments that align with how clinicians actually make decisions.

New in JMIR MedEdu: Digital Choice Architecture in medical education #mededu: Applying Behavioral Economics to Online Learning Environments

A systematic review of engagement in ##MedicalEducation #mededu Date Submitted: Jan 28, 2026. Open Peer Review Period: Jan 30, 2026 - Mar 27, 2026.

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A systematic review of engagement in ##MedicalEducation #mededu Date Submitted: Jan 28, 2026. Open Peer Review Period: Jan 30, 2026 - Mar 27, 2026.

A systematic review of engagement in ##MedicalEducation #mededu (preprint) #openscience #PeerReviewMe #PlanP

Global Learner Feedback on Continuing medical education #mededu–Accredited e-Learning Modules in Pediatric Endocrinology and Diabetes: Cross-Sectional Study Background: The European Society for Paediatric Endocrinology (ESPE) e-Learning website, www.espe-elearning.org, is a free, globally accessible online resource to enhance learning in pediatric endocrinology and diabetes. The content is created by world-leading experts in pediatric endocrinology and diabetes and is closely aligned with published international consensus guidelines. In August 2022, 30 hours of e-learning courses received accreditation from the European Accreditation Council for CME (EACCME®). These CME courses cover three categories: (1) Pediatric Endocrinology, (2) Pediatric Diabetes, and (3) Pediatric Endocrinology in Resource-Limited Settings. Objective: To assess learners' demographics and feedback from mandatory surveys after completion of CME e-learning courses, and identify areas for improvement. Methods: The ESPE e-learning committee created a mandatory survey for each CME e-learning module. The survey includes baseline demographics and feedback on the quality of the learning content, assessed using a five-level Likert scale. Data was extracted from the start of the CME modules in August 2022 until September 2025. Results: 567 surveys were completed: 286 (50,4%) in the category Pediatric Endocrinology, 225 (39,7%) in the category Pediatric Diabetes ISPAD Guidelines, and 56 (9.9%) in the category Pediatric Endocrinology in Resource Limited Settings. There was global participation, with most learners practicing in Europe (n=333 (59%)), followed by Asia (n= 124 [22%]), Africa (n=53 [9%]), the Americas (n=45 [8%] North America, n=11 [2%] South America),and Oceania (n=1 [0%]). Most of the users indicated to be medical experts (37%), followed by fellows/residents (39%), medical students and nurses (5% and 6%, respectively); 10% of learners practice in resource-limited countries. Overall, the learning content was well received for all modules regarding accessibility, organization, level of interest, improvement of learner’s clinical practice, appropriateness of content and provision of feedback (median Likert score 4; IQR 1). Learners’ free-text feedback identified some areas of improvement, including reducing text-heavy content, providing more graphical content and more interactive case reports. Most learners' free text feedback consists of encouraging and thankful comments. Conclusions: The ESPE CME-accredited e-learning modules are well-received providing globally free CME education in pediatric endocrinology and diabetes. These findings support the continued development and promotion of open-access CME platforms improving global equity in specialist medical education #mededu and focusing on educational impact.

New in JMIR MedEdu: Global Learner Feedback on Continuing medical education #mededu–Accredited e-Learning Modules in Pediatric Endocrinology and Diabetes: Cross-Sectional Study

Exploring the Icarus Paradox in Indonesia's Specialist medical education #mededu System Using the Public Perspective From Online Media: Convergent Mixed Methods Study Background: The Icarus Paradox in health care refers to the tension between the ambition to succeed as a specialist doctor and the limitations of the medical education #mededu system. Indonesia aspires to produce quality doctors, yet limited infrastructure and resources hinder the educational journey of prospective specialists. Objective: This study aimed to identify the Icarus Paradox in Indonesia's specialist medical education #mededu by examining prospective specialist medical students and the quality of health services and by analyzing how this paradox is reflected in society’s perspectives. Methods: Using a convergent mixed methods design, this study integrated quantitative content analysis of 5047 online reviews across multiple platforms with qualitative thematic and cognitive analysis using NVivo 14, combining sentiment classification and topic coding. Results: Twitter contributed 573 (11.3%) of 5047 reviews, with 218 (38%) negative, 251 (43.8%) neutral, and 104 (18.2%) positive entries. TikTok generated 282 (5.6%) reviews, the majority being neutral (n=225, 79.5%). YouTube produced 96 (1.9%) reviews, with 89 (92.7%) neutral entries. News platforms exhibited the largest volume (n=3040, 60.2%) of reviews, with 2885 (94.9%) neutral, 105 (3.5%) positive, and 50 (1.6%) negative entries. Blogs and websites contributed 353 (7%) and 692 (11.3%) reviews, respectively, with neutral sentiment dominating (n=329, 93.2%, for blogs and n=599, 86.6%, for websites). Three cognitive perspectives demonstrated the Icarus Paradox in the Indonesian medical education #mededu system: education system, society’s views of students, and health care services. Although there are aspirations to improve education and health care quality, these ambitions often collide with structural challenges, such as resource shortages, heavy workloads, and limited accessibility, which link directly to cognitive themes of stress, resilience, and ethical dilemmas. We proposed a conceptual model to illustrate these dynamics. Conclusions: Our findings offer insights into the Icarus Paradox in Indonesia’s medical education #mededu system, highlighting its complexity and reinforcing the need for systemic reform. Beyond academic relevance, the findings also emphasize the importance of strengthening student mental health support, ensuring equitable access to health care, and enhancing regulatory oversight of training. This was not a clinical trial. Although limited by reliance on online reviews, the results underscore the urgent need for targeted policy interventions in medical education #mededu and health care services. Trial Registration: ClinicalTrials.gov registration: NCT123456

New in JMIR MedEdu: Exploring the Icarus Paradox in Indonesia's Specialist medical education #mededu System Using the Public Perspective From Online Media: Convergent Mixed Methods Study

Large Language Models in German Continuing ##MedicalEducation #mededu Assessment: Fully Crossed Experimental #Study #Protocol Date Submitted: Jan 18, 2026. Open Peer Review Period: Jan 19, 2026 - Mar 16, 2026.

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Large Language Models in German Continuing ##MedicalEducation #mededu Assessment: Fully Crossed Experimental #Study #Protocol Date Submitted: Jan 18, 2026. Open Peer Review Period: Jan 19, 2026 - Mar 16, 2026.

Large Language Models in German Continuing ##MedicalEducation #mededu Assessment: Fully Crossed Experimental #Study #Protocol (preprint) #openscience #PeerReviewMe #PlanP

Impact of Community-Oriented medical education #mededu on Medical Students’ Perceptions of Community Health Care: Qualitative Study Background: Physician maldistribution remains a global challenge, with Japan’s rural regions facing critical health care shortages. Regional quota programs aim to attract medical students to underserved areas; however, their effectiveness in fostering long-term commitment is uncertain. Community-oriented medical education #mededu (COME) programs aim to address this issue by developing students’ understanding and dedication to rural health care. Objective: This study investigated the impact of an enhanced COME program, featuring increased early clinical exposure and faculty development, on first-year regional quota medical students’ perception of community health care at Chiba University. Methods: We conducted a cross-sectional qualitative study comparing 2 cohorts, 20 students enrolled from the existing COME course (April-December 2021) and 20 from the revised course (April-December 2022). The revised course included an additional day of community-based clinical exposure supervised by COME-trained attending physicians. Students’ written reflections were analyzed using qualitative content analysis and categorized according to the Fink Taxonomy of significant learning, comprising 6 domains, including foundational knowledge, application, integration, human dimension, caring, and learning how to learn. Reflections were synthesized into higher-order themes crosswalked to the Fink domains. Results: Demographics were similar between the 2021 and 2022 cohorts. In 2021, 311 learning codes were identified across foundational knowledge (n=128), application (n=91), integration (n=40), human dimension (n=16), caring (n=30), and learning how to learn (n=6). In 2022, codes increased to 385, with notable growth in caring (n=58) and human dimension (n=57), alongside increases in learning how to learn (n=15) and integration (n=45). Theme-based synthesis identified four overarching themes: (1) community health care as an interconnected, resource-constrained system; (2) patient-centered relationships and trust through communication and teamwork; (3) emerging professional identity and responsibility toward community service; and (4) developing a self-directed learning orientation for community practice. Qualitative analysis revealed that students gained a deeper understanding of patient-centered care, interprofessional collaboration, and social challenges in rural health care. The consistency in the foundational knowledge domain underscored a stable conceptual foundation, while the increase in affective and reflective domains reflected greater emphasis on interpersonal, value-oriented, and reflective learning in the revised cohort. Conclusions: Enhancements of the COME program, including additional early clinical exposure and faculty development, were associated with improved students’ perceptions of community health care. The increased focus on the caring and human dimension domains underscores the role of practical experiences in fostering collaboration, communication, and patient-centered care. The theme-based synthesis further suggests that the revised program prompted more frequent reflections on professional identity formation and self-directed learning while maintaining a stable foundation of community health care concepts. Mentorship by community hospital attendings, alongside structured clinical exposure, appears crucial in shaping medical students’ understanding and commitment to rural medicine. Ongoing longitudinal evaluations are warranted to assess the sustained impact of COME programs on career trajectories in underserved areas.

New in JMIR MedEdu: Impact of Community-Oriented medical education #mededu on Medical Students’ Perceptions of Community Health Care: Qualitative Study

SUNDAY QUESTION - The patient looks clammy and is cool peripherally. Pulse is 130 beats per minute and blood pressure 80/40mmHg Which of the following is the most appropriate initial management option? a). Endoscopy b). Omeprazole 40mg IV c). Oxygen via non-breather mask d). Terlipressin 2mg IV

SUNDAY QUESTION: Which of the following is the most appropriate initial management option?

#eLearning #MedEdu

#Medical Faculty Perspectives on Artificial Intelligence Integration in Undergraduate ##MedicalEducation #mededu: A Qualitative #Study from the United Arab Emirates Date Submitted: Dec 23, 2025. Open Peer Review Period: Dec 23, 2025 - Feb 17, 2026.

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#Medical Faculty Perspectives on Artificial Intelligence Integration in Undergraduate ##MedicalEducation #mededu: A Qualitative #Study from the United Arab Emirates Date Submitted: Dec 23, 2025. Open Peer Review Period: Dec 23, 2025 - Feb 17, 2026.

#Medical Faculty Perspectives on Artificial Intelligence Integration in Undergraduate ##MedicalEducation #mededu: A Qualitative #Study from the United Arab Emirates (preprint) #openscience #PeerReviewMe #PlanP

Twelve tips for recognizing and addressing the adverse effects of ##MedicalEducation #mededu interventions Date Submitted: Dec 15, 2025. Open Peer Review Period: Dec 16, 2025 - Feb 10, 2026.

Reminder>> Twelve tips for recognizing and addressing the adverse effects of ##MedicalEducation #mededu interventions (preprint) #openscience #PeerReviewMe #PlanP

The Need for Health Care Innovation Training in medical education #mededu The healthcare landscape is rapidly transforming due to technological advancement, requiring physicians to not only be skilled clinically but also navigate and lead a highly dynamic, innovation-driven environment. Yet, few medical schools currently provide opportunities for formal training in innovation and entrepreneurship (I&E). In this perspective, we examine the need for I&E education in medical curricula by exploring student interest, effective program models, and implementation strategies. To better understand medical student interest in innovation and willingness to participate in innovation programs during medical school, we surveyed 480 medical students at our institution, the Johns Hopkins University School of Medicine. We observed a strong interest in healthcare innovation, with 97% of respondents valuing knowledge or experience in innovation and 63% expressing intent to incorporate I&E into their careers. To assess the real-world impact of I&E education on medical professionals, we surveyed alumni of the Johns Hopkins Center for Bioengineering Innovation and Design (CBID) master’s program who had also completed medical school. Graduates reported that their experiences cultivated transferable skills—design thinking, interdisciplinary collaboration, and leadership—that shaped their professional trajectories. We propose three models for incorporating I&E education into existing medical curricula—short-term workshops, one-year gap programs, and longitudinal tracks—and discuss their advantages and tradeoffs. Early and structured exposure to I&E education in medical school empowers students to identify unmet clinical needs, collaborate across disciplines, and develop real-world solutions. As the pace of innovation continues to accelerate, integration of I&E education into medical curricula offers a timely opportunity for medical schools to cultivate physician leaders in this space.

New in JMIR MedEdu: The Need for Health Care Innovation Training in medical education #mededu

Twelve tips for recognizing and addressing the adverse effects of ##MedicalEducation #mededu interventions Date Submitted: Dec 15, 2025. Open Peer Review Period: Dec 16, 2025 - Feb 10, 2026.

Twelve tips for recognizing and addressing the adverse effects of ##MedicalEducation #mededu interventions (preprint) #openscience #PeerReviewMe #PlanP

Twelve Practical Tips for Integrating AI Into medical education #mededu: Tutorial to Support Educators Across Teaching, Research, Administration, and Ethical Domains Artificial intelligence (AI) is rapidly reshaping medical education #mededu, offering new opportunities to personalize learning, enhance research, and streamline administration. The aim of this study is to provide 12 practical, evidence-informed tips by drawing on current literature and real-world examples to guide the integration of AI into medical education #mededu, supporting educators across teaching, research, administration, and ethical domains. Key strategies include using adaptive learning platforms to tailor educational content, using AI tools to provide timely feedback, and incorporating AI-generated clinical scenarios in case-based learning. The importance of fostering AI literacy among students is emphasized, as well as utilizing AI-powered tools for efficient literature reviews, data analysis, and manuscript preparation. Administrative applications such as automating routine tasks, supporting strategic planning through data analysis, and enhancing faculty development with AI-driven platforms are also discussed. Ethical considerations are highlighted, with a focus on ensuring transparency, fairness, and accountability in all AI applications. By following these 12 tips, medical educators can leverage the benefits of AI to improve educational outcomes, increase efficiency, and prepare future clinicians for a technology-driven health care environment.

New in JMIR MedEdu: Twelve Practical Tips for Integrating AI Into medical education #mededu: Tutorial to Support Educators Across Teaching, Research, Administration, and Ethical Domains