JMIR Medical Education

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Virtual reality (VR) simulation—using head-mounted displays to present a computer-generated, 3D, interactive environment—may be a cost-effective alternative to in-person (IP) medical simulation training. However, studies directly comparing learning outcomes have demonstrated mixed results and mainly focused on knowledge or skill acquisition rather than integrated practice.

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Generative artificial intelligence (AI) tools, such as ChatGPT, are increasingly used in higher education and have raised significant concerns about assessment validity and academic integrity. In Digital Health and Health Information Management (DIGHIM) programs, assessments are designed to evaluate a mix of technical skills, contextual reasoning, and professional judgment that underpin medical and health practice. Understanding how generative AI performs across different assessment types is, therefore, critical to identifying which formats are most susceptible to AI-generated content and how assessments may be redesigned to remain authentic and educationally meaningful.

Virtual patients (VPs) demonstrate effectiveness in improving clinical reasoning skills; however, traditional VP platforms often lack individualized feedback mechanisms. Advances in large language models (LLMs) enable automated analysis of student-VP interactions, providing scalable feedback on clinical performance. While artificial intelligence (AI)–enhanced social robotic VP platforms show promise for clinical reasoning training, no studies have examined whether AI-generated feedback integrated in such platforms improves clinical performance in standardized assessments.

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.

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.

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.

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.