JMIR Medical Education

Simulation-based learning (SBL) has become standard practice in educating health care professionals to apply their knowledge and skills in patient care. While SBL has demonstrated its value in education, many educators find the process of developing new, unique scenarios to be time-intensive, creating limits to the variety of issues students may experience within educational settings. Generative artificial intelligence (AI) platforms, such as ChatGPT (OpenAI), have emerged as a potential tool for developing simulation case studies more efficiently, though little is known about the performance of AI in generating high-quality case studies for interprofessional education.

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.

Despite global advocacy for its integration into medical curricula, disaster medicine (DM) education remains underdeveloped, especially in fragile settings where such training is urgently needed. In Lebanon, a country facing political and economic crises, students face significant barriers to in-person education.

There is a need to modernise the dissemination of clinical guidelines, making them more accessible and engaging for healthcare professionals. Concise Medical Information Cines (CoMICs) are peer-reviewed videos created by medical students that distil complex guidelines into learner-friendly visuals.

Virtual reality (VR) technology is increasingly used in health care professionals’ education as a novel tool for teaching, learning, and assessment.

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 system. Indonesia aspires to produce quality doctors, yet limited infrastructure and resources hinder the educational journey of prospective specialists.

Game-based learning has emerged as an effective learning strategy for enhancing knowledge and engagement in healthcare education. However, they have not been specifically designed to support cognitive improvements for diverse learning styles in oral microbiology and immunology.

Cancer immunotherapy represents a transformative advancement in oncology, offering new avenues for treating malignancies by harnessing the immune system. Despite its growing clinical relevance, immunotherapy remains underrepresented in undergraduate medical education, particularly in curricula integrating foundational immunology with clinical application. To address this gap, we developed and implemented a fully online elective for fourth-year medical students focused on core immunology concepts, immunotherapy mechanisms, FDA-approved treatments, immune-related adverse events, and patient-centered therapeutic decision-making.

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 (COME) programs aim to address this issue by developing students’ understanding and dedication to rural health care.

Traditional Chinese medicine (TCM) has been widely used against various diseases in China for thousands of years and showed satisfactory effectiveness. However, many surveys found that TCM receives less recognition from Western medicine (WM) doctors and students. Presently, TCM is offered as a compulsory course for WM students in Western medical schools.

This paper proposes a framework for leveraging large language models (LLMs) to generate misconceptions as a tool for collaborative learning in healthcare education. While misconceptions—particularly those generated by AI—are often viewed as detrimental to learning, we present an alternative perspective: that LLM-generated misconceptions, when addressed through structured peer discussion, can promote conceptual change and critical thinking. The paper outlines use cases across healthcare disciplines, including both clinical and basic science contexts, and a practical 10-step guidance for educators to implement the framework. It also highlights the need for medium- to long-term research to evaluate the impact of LLM-supported learning on student outcomes. This framework may support healthcare educators globally in integrating emerging AI technologies into their teaching, regardless of disciplinary focus.

The use of Artificial Intelligence (AI) to analyze healthcare data has become common in behavioral health sciences. However, the lack of training opportunities for mental health professionals limit clinicians' ability to adopt AI in clinical settings. AI education is essential for trainees, equipping them with the literacy needed to implement AI tools in practice, collaborate effectively with data scientists, and develop as interdisciplinary researchers with computing skills.