JMIR Medical Education

Medical education continues to favor didactic lectures as the predominant method of instruction. However, in recent years, there has been a shift to active learning methodologies, such as gamification.

Artificial intelligence (AI) has the potential to transform training through adaptive learning, immersive simulations, automated assessments, and data driven insights, offering solutions to persistent issues such as high student to faculty ratios, overcrowded classrooms, and limited clinical exposure. Globally, many universities have already embedded AI literacy and competencies into undergraduate, postgraduate, and continuing education programs, while in Vietnam the use of AI in medical education remains limited and fragmented. This Viewpoint aims to assess the opportunities and challenges of using AI in medical education in this country. Most students have little formal exposure to AI, and empirical evidence on faculty or institutional readiness is scarce. Experiences from other countries including Malaysia, Palestine, and Oman demonstrate that incremental adoption and faculty development can facilitate cultural acceptance and curricular innovation, providing useful lessons for Vietnam. At the same time, significant barriers remain. These include inadequate infrastructure in provincial universities, low levels of AI literacy among both students and educators, underdeveloped regulatory and ethical frameworks, and resistance to pedagogical change. Cost effectiveness and sustainability are additional concerns in a middle-income context, where upfront investments must be balanced against long term benefits and equitable access. Advancing AI in Vietnamese medical education will therefore require a coordinated national strategy that prioritizes infrastructure, AI literacy, faculty development, quality assurance, and sustainable funding models, alongside ethical and legal safeguards. By addressing these foundations, Vietnam can harness AI not only to modernize medical education but also to strengthen preparedness for a digitally enabled health workforce.

The impact of Pass/Fail or Tiered Grade assessment for exams in undergraduate medical education causes much debate while there is little data to inform decision making. The increasing number of medical schools transitioned to Pass/Fail assessment has raised a concern about medical students’ academic performance. In 2018, the undergraduate medical curriculum reform at the Faculty of Medicine, Aalborg University changed some exams from Pass/Fail to Tiered Grade and vice versa for other exams. These changes provide an opportunity to evaluate the different assessment forms.

Large language models (LLMs) offer the potential to improve virtual patient-physician communication and reduce health care professionals’ workload. However, limitations in accuracy, outdated knowledge, and safety issues restrict their effective use in real clinical settings. Addressing these challenges is crucial for making LLMs a reliable health care tool.

Clinical reasoning is increasingly recognised as an important skill in the diagnosis of common and serious conditions. eCREST (electronic Clinical Reasoning Educational Simulation Tool), a clinical reasoning learning resource, was developed to support medical students to learn clinical reasoning. However, primary care teams now encompass a wider range of professional groups, such as Physician Assistants [PAs] who also need to develop clinical reasoning during their training. Understanding PAs’ clinical reasoning processes is key to judging the transferability of learning resources initially targeted to medical students.

Clinical competency is essential for oncology students to deliver high-quality patient care. However, traditional teaching methods may not fully support the development of critical skills such as communication, empathy, and clinical judgment. Peer role-play has emerged as a promising approach to bridge these gaps by enhancing interpersonal and diagnostic competencies within clinical settings.

Learner autonomy—the ability to self-direct and regulate learning—is a key determinant of success in online education, yet its quantifiable impact in voluntary noncredit courses remains unclear. Understanding how autonomy translates into measurable behaviors and outcomes in clinical skills training may inform more effective online learning design and learning outcomes.

To further optimize the clinical and scientific training of high-level doctoral graduates, the Office of the National Postdoctoral Administration launched a clinical postdoctoral program in 2015. This program provides postdoctoral clinical medicine trainees with three years of individualized, intensive training through a full mentorship system, interdisciplinary collaboration, and a multi-team teaching platform.

The use of artificial intelligence (AI)–based large language model chatbots such as ChatGPT has become increasingly popular in many disciplines. However, concerns exist regarding ethics, legal considerations, accuracy, and reproducibility with its use in health care practice, education, and research.

Video-sharing sites such as YouTube and TikTok have become indispensable resources for learners and educators. The recent growth in generative artificial intelligence (genAI) tools, however, has resulted in low-quality, AI-generated material (commonly called “slop”) cluttering these platforms and competing with authoritative educational materials. The extent to which slop has polluted science education video content is unknown, as are the specific hazards to learning from purportedly educational videos made by artificial intelligence (AI) without the use of human discretion.

Digital tools are increasingly used to support clinical decision-making in dental education. However, the accuracy and efficiency of different support tools, including generative artificial intelligence, in the context of dental trauma management remain underexplored.

Artificial intelligence (AI) is increasingly being integrated into medical education. As AI technologies continue to evolve, they are expected to enable more sophisticated student tutoring, performance evaluation, and reforms of curricula. However, medical education entities have been ill-prepared to embrace this technological revolution, and there is anxiety concerning its potential harm to the community.