JMIR Medical Education

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.

Structured educational programs for physicians in healthy longevity medicine (HLM) remain scarce. No published data yet document the impact of longevity-focused medical education on physicians. This study assesses the ramifications of the HLM curriculum, certified by the American Council for Continuing Medical Education, on physicians’ confidence in their knowledge of HLM and clinical practice.

Simulation-based training is essential for preparing medical interns to manage high-stakes emergencies. Although virtual reality (VR)-based simulation has been rapidly integrated into medical education, there remains limited evidence directly assessing its effectiveness relative to established high-fidelity simulation (HFS) methodologies.

Artificial intelligence (AI) is rapidly transforming health care by enhancing diagnostic accuracy, optimizing clinical workflows, and supporting decision-making across all health disciplines. As AI-driven tools are progressively introduced into health systems, educating future professionals about AI has become a critical priority to ensure safe, ethical, and effective use. Although several validated English-language questionnaires exist to assess medical students’ perceptions and readiness on AI in medicine, no French-language equivalents are currently available, which limits their use in francophone settings and hampers international comparisons. To bridge this gap and enable comparable, evidence-based assessment of AI perceptions among French health care students, rigorous cross-cultural adaptation of validated instruments is essential.

Gender-based violence (GBV) is a public health issue affecting 1 in 3 women globally. Its impact on women’s health is challenging, including physical, mental, and social consequences. Health care professionals have a unique opportunity in identifying and supporting GBV survivors, but there is a lack of adequate training.

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 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 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 to cultivate the skills required for a profession increasingly practiced in a context of overstimulation and complexity.

The exponential growth of medical knowledge presents a paradox for modern medical education. While access to information is immediate, applying it in a clinically meaningful way remains a challenge. Large language models (LLMs), such as ChatGPT, are widely used for information retrieval, yet their role in dynamic, high-pressure clinical learning remains poorly understood.

Simulation-based education is crucial for training health care professionals in advanced cardiac life support. However, access to high-fidelity in-person simulation is frequently limited by geographic, logistical, and financial constraints. Augmented reality (AR) offers the potential to deliver remote, immersive training experiences that may overcome these barriers, but its effectiveness compared with traditional simulation remains uncertain.

Effective interprofessional collaboration (IPC) is essential for patient safety; yet, poor teamwork and communication remain key challenges in high-pressure settings like the emergency department (ED), contributing to medication errors. Although Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS)–based interprofessional education addresses these issues, adaptation in clinical settings remains difficult. To bridge this gap, we developed Emergency Room Virtual Simulation-Based Interprofessional Education (ER-VIPE), a multimodal, TeamSTEPPS-integrated intervention designed to enhance IPC and reduce medication errors.

Transitioning from preclinical to clinical training is a critical milestone of “becoming and being” in a medical student’s journey. Despite simulation-based learning, real-world clinical exposure remains indispensable in shaping professional identity. The clinical learning environment is a complex interplay of social, cultural, and organizational factors that influence students’ development as future health care professionals.

Limited clinical placements for mental health courses in the United Arab Emirates have made it difficult to provide consistent experiential learning for undergraduate nursing students. As a result, nurse educators are considering technology-enabled learning approaches to deliver clinical skills training. This Viewpoint presents a reflective, theory-informed account of the first-year integration of an artificial intelligence (AI)–enabled, voice-interactive simulated patient into an undergraduate mental health nursing practicum. Grounded in Kolb’s experiential learning cycle and aligned with established simulation best practices, the initiative was designed to support therapeutic communication, psychiatric assessment, and clinical reasoning through structured prebriefing, immersive interaction, and guided debriefing. The paper describes the educational rationale, scenario development, implementation processes, and contextual challenges encountered during real-world deployment across university and clinical environments. AI-supported simulations offered a standardized and psychologically safe context for students to engage with complex psychiatric scenarios, particularly when direct patient interaction is constrained. We discuss operational insights related to technical reliability, environmental requirements, faculty preparation, and assessment integration alongside considerations for scalability and sustainability in resource-limited settings. While AI-supported objective structured clinical examinations have been incorporated to support assessment consistency, formal psychometric validation and outcome comparisons have not been undertaken at this stage. By sharing lessons learned from early implementation, this Viewpoint contributes practical insights for nursing educators facing similar structural constraints. AI-enabled simulation is presented as a strategic complement to, rather than a replacement for, traditional clinical placements, with future empirical research needed to evaluate educational outcomes and long-term impact.

Advancements in artificial intelligence (AI) are transforming health care, particularly through AI-driven clinical decision support systems (AI-CDSS) that aid in predicting disease progression and personalizing treatment. Despite their potential, adoption remains limited due to clinician concerns about algorithm misuse, misinterpretation, and lack of transparency.