Walaa Elhadi Ahmed Abdallah,1 Asaad Ibrahim Abdalla,2 Mojtaba Majdy Elfakey Omer,3 Marwa S Abusbeeb,4 Mohamedelmustafa Eldouma,5 Reham Mohammed Abdalla Mohammed Ali,6 Mushrega Abdalla,5 Reem Ali Adam,5 Ismail Mahmoud Ismail Mustafa,7 Othman Nasreldin Amin,8 Fatima Eltayeb Elsiddig Mohammed,9 Alemam Ahmed,10 Mutasim Idriss,11 Mohammed Mahmmoud Fadelallah Eljack12 1Faculty of Medicine, International University of Africa, Khartoum, Sudan; 2Faculty of Medicine, Al Imam Abdulrahman Alfaisal Hospital, Riyadh, Saudi Arabia; 3Faculty of Medicine, University of Alzaiem Alzhari, Khartoum, Sudan; 4Faculty of Pharmacy, University of Khartoum, Khartoum, Sudan; 5Faculty of Medicine, University of Khartoum, Khartoum, Sudan; 6Faculty of Medicine and Health Sciences, University of Gadarif, Gadarif, Sudan; 7Faculty of Medicine, University of Gazira, Wad Madani, Sudan; 8Faculty of Medicine and Health Sciences, University of Dongola, Dongola, Sudan; 9Faculty of Medicine, Iqraa College for Science & Technology, Wad Madani, Sudan; 10Faculty of Medicine, Zagazig University, Sharqia Governorate, Zagazig, Egypt; 11Rochester Regional Health, Unity Hospital, Rochester, NY, USA; 12Faculty of Medicine and Health Sciences, University of Bakht Alruda, Eldueim, SudanCorrespondence: Walaa Elhadi Ahmed Abdallah, Faculty of Medicine, International University of Africa, Khartoum, Sudan, Email walaelhadi267@gmail.comBackground: Electrocardiogram (ECG) interpretation is a fundamental clinical skill, yet proficiency remains inconsistent. We evaluated ECG interpretation competency among medical doctors in Sudan to identify knowledge gaps and predictors of proficiency.Methods: In this multi-center, cross-sectional study, 482 medical doctors from tertiary teaching hospitals across six Sudanese states completed a validated online assessment using the Coll-Badell tool. Data were analyzed using R software.Results: Most participants were aged 25– 34 years (89.8%) and female (54.6%), with 40.7% reporting prior ECG training. The overall mean (SD) score was 4.72 (2.77) out of 10. Recognition rates were lowest for pathological Q waves (29.3%) and acute myocardial infarction (35.7%). In multivariable linear regression, performance was most notably driven by an internal medicine specialty (beta = 1.9, 95% CI 1.1 to 2.7, p < 0.001) and 6 to 10 years of emergency care experience (beta = 1.8, 95% CI 0.62 to 3.0, p = 0.003), followed by intermediate gains from 1 to 5 years of emergency experience (beta = 0.83, 95% CI 0.32 to 1.3, p = 0.002), prior ECG training attendance (beta = 0.56, 95% CI 0.07 to 1.0, p = 0.024), and male gender (beta = 0.53, 95% CI 0.05 to 1.0, p = 0.030).Conclusions: Sudanese doctors demonstrated low ECG competency, particularly in identifying acute myocardial infarction and pathological Q waves. Higher scores were linked to internal medicine specialization, emergency experience, and prior training. Structured ECG training programs should be integrated into local curricula, though further interventional studies are needed to evaluate their scalability.Keywords: clinical competence, electrocardiography, professional practice
Fateme Hejazi,1 Narges Tavangar,1 Ali Saber,2 Mohammad Saeid Moeinfar1,3 1Student Research Committee, School of Medicine, Kashan University of Medical Sciences, Kashan, Iran; 2Department of Medical Ethics, School of Medicine, Kashan University of Medical Sciences, Kashan, Iran; 3Department of Medical Education, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, IranCorrespondence: Mohammad Saeid Moeinfar, Kashan University of Medical Sciences, Pezeshk Blvd, Qutb-e Ravandi Blvd, Kashan, 87159-81151, Iran, Email Msmf020@gmail.comBackground: Artificial intelligence (AI) is increasingly integrated into clinical practice and medical education, yet research on AI and medical professional identity has largely evolved in parallel.Objective: To examine how AI is transforming fundamental components of medical professional identity and propose a framework for understanding its implications for professional identity formation in medical education.Methods: This narrative review synthesizes conceptual, empirical, and theoretical literature on AI, professional identity formation, and clinical practice through iterative literature searching and citation tracking. Peer-reviewed studies, regulatory data, and national survey reports were considered.Results: AI affects professional identity unevenly. Diagnostic, predictive, generative, and emerging autonomous systems most directly influence expertise, clinical reasoning, and professional autonomy, shifting physicians from exclusive holders of specialized knowledge toward validators of increasingly accessible but variably trusted information. Evidence of automation bias and reduced diagnostic performance highlights this transition. We conceptualize the divergence between the formal locus of clinical responsibility, which remains with physicians, and the functional locus of influence decisions, shared with AI, as the formal–functional gap (FFG). Its magnitude varies by AI configuration, identity component, and career stage, potentially placing trainees at risk of never-skilling and experienced physicians at risk of deskilling. Ethical responsibility, humanistic care, lifelong learning, and professional authority are affected more indirectly.Conclusion: AI-mediated professional identity change reflects a dynamic interaction between physicians’ developmental trajectories and evolving AI capabilities. Medical education should prioritize sustained expertise, clinical reasoning, professional autonomy, AI-interpretive judgment, and human oversight. The FFG remains a conceptual framework whose relationships require empirical validation.Plain Language Summary: Artificial intelligence (AI) tools are now common in hospitals and medical schools. They help with diagnosing diseases, writing notes, and answering medical questions. While these tools can improve care, they also raise important questions about what it means to be a doctor.This review looked at how AI is changing the professional identity of physicians — the sense of who they are and what they are responsible for. We examined existing research, expert opinions, and real-world examples of AI use in medicine.We found that AI does not affect all parts of a doctor’s role equally. It has the strongest impact on medical knowledge, clinical decision-making, and professional independence. Doctors are shifting from being the only source of specialized knowledge to checking and interpreting information that AI systems provide. At the same time, legal responsibility for patient care still rests with the physician. We call this mismatch between formal responsibility and actual influence the “formal–functional gap”.This gap can create different risks. Medical students may never fully develop key skills if they rely too heavily on AI from the start. Experienced doctors may lose some of their skills over time through reduced practice. Other important parts of being a doctor — such as ethical responsibility, empathy, and lifelong learning — are affected more indirectly.These findings matter for medical education. Training programmes should protect doctors’ ability to think independently, teach them how to critically evaluate AI suggestions, and ensure that human oversight remains meaningful. The goal is not to reject AI, but to make sure that technology strengthens rather than weakens the core values and skills of the medical profession.Keywords: artificial intelligence, professional identity, professional identity formation, medical education, clinical reasoning, large language models
Shereen El Tarhouny,1,2 David CM Taylor,3 Majd Jan,4 Tayseer Mansour,5,6 Marwa Zalat6,7 1Medical Biochemistry Department, Faculty of Medicine, Zagazig University, Zagazig, Egypt; 2Health Profession Education Center, Ibn Sina National College for Medical Studies, Jeddah, Saudi Arabia; 3College of Medicine, Gulf Medical University, and Center for Innovation and Leadership in Health Professions Education, Ajman, United Arab Emirates; 4Department of Clinical Pharmacy and Pharmacology, Ibn Sina National College for Medical Studies, Jeddah, Saudi Arabia; 5Medical Education Department, Faculty of Medicine, Suez Canal University, Ismailia, Egypt; 6Family and Community Medicine and Medical Education Department, College of Medicine, Taibah University, Al-Madinah, Saudi Arabia; 7Department of Community, Occupational and Environmental Medicine, Faculty of Medicine, Zagazig University, Zagazig, EgyptCorrespondence: Marwa Zalat, Family and Community Medicine and Medical Education Department, College of Medicine, Taibah University, Al-Madinah, Saudi Arabia, Email Marwazalat79@gmail.comAbstract: Medical teachers work across teaching, assessment, clinical care, research, administration, and mentorship. Persistent workload pressure, insufficient recognition, moral distress, and limited institutional support can compromise faculty well-being and threaten the sustainability of health professions education. The aim of this narrative review is to synthesize leadership and organizational strategies that can support medical-teacher well-being and translate them into a practical framework for educational leaders. A targeted literature search was conducted using PubMed, Scopus, and Web of Science Core Collection to identify publications on faculty well-being, educational leadership, workload, psychological safety, mentorship, moral distress, faculty development, and organizational culture in health professions education and academic medicine. Recent reviews, systematic reviews, consensus statements, and guideline-oriented publications were emphasized, while retaining selected seminal sources for foundational concepts. The ten actions were formulated through narrative synthesis of the selected literature, contextualized by the authors’ experience in medical education and faculty development. We propose ten complementary actions: institutionalize well-being as a leadership responsibility; lead with compassion and psychological safety; model sustainable boundaries and cognitive renewal; distribute workload fairly and account for hidden work; recognize teaching and educational labor; build collegiality and connection; address moral distress and compassion fatigue; invest in mentorship and career development; integrate well-being literacy into faculty development; and evaluate, learn, and adapt through participatory governance. Faculty well-being cannot be sustained through individual resilience strategies alone. Leaders must alter the conditions in which medical teachers work by combining relational leadership with fair systems, meaningful recognition, protected development, and continuous evaluation. The framework is an author-derived, evidence-informed synthesis rather than a validated or causal implementation model and requires prospective evaluation across diverse institutional and cultural settings.Keywords: medical teacher well-being, educational leadership, health professions education, burnout, psychological safety, workload, faculty development
Tzur Ko-Geen Rochvarger,* Victoria Moushiaveshi,* Samuel Arda, Christos Tzivanopoulos, Nicos Michael, Chloe Antoniou Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus*These authors contributed equally to this workCorrespondence: Chloe Antoniou, Email antoniou.c@unic.ac.cyBackground: Artificial intelligence (AI) is increasingly used in medical education for content generation, assessment, adaptive learning, simulation, and skills training. Its sustainability implications, however, remain underexplored. This scoping review mapped literature on AI in medical education through social, environmental, and governance perspectives, using the United Nations Sustainable Development Goals and an environmental, social, and governance framework.Methods: Following PRISMA-ScR guidance, Scopus and PubMed were searched for studies published from 2000 to 2025 using terms related to AI, medical education, healthcare training, and sustainability. Records underwent title and keyword, abstract, and full-text screening, then were synthesized thematically.Results: Forty-five studies were included and four thematic areas were identified: Curriculum and pedagogy, Tools and integrated simulations, Perceptions of AI, and Sustainability, Ethics and Concerns. AI was commonly viewed as a useful educational adjunct, particularly for personalized learning, assessment support, content generation, and simulation. Key concerns included algorithmic bias, privacy and confidentiality risks, academic integrity, unequal access to infrastructure and training, limited governance, and overreliance that may weaken critical thinking, communication, and humanistic competencies. Evidence of long-term educational benefits and transfer to clinical practice was limited. Sustainability was the least directly examined theme.Conclusions: AI has considerable potential to support innovation in medical education, but its sustainable integration remains to be fully explored. Responsible implementation requires stronger governance, equitable access, protection of privacy, and robust evaluation of long-term educational outcomes. AI should support, rather than replace, human teaching, professional judgment, and institutional accountability.Keywords: sustainability, UN SDG, artificial intelligence, medical education, ESG and AI and sustainability
