JMIR Medical Education

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.

Objective Structured Clinical Examinations (OSCEs) are used as an evaluation method in medical education, but require significant pedagogical expertise and investment, especially in emerging fields like digital health. Large language models (LLMs), such as ChatGPT (OpenAI), have shown potential in automating educational content generation. However, OSCE generation using LLMs remains underexplored.

Foundational knowledge of anesthesia techniques is essential for medical students. Team-based learning (TBL) improves engagement. Web-based virtual environments (WBVEs) allow many learners to join the same session in real time while being guided by an instructor.

The rapid advancement of artificial intelligence (AI) has had a substantial impact on medicine, necessitating the integration of AI education into medical school curricula. However, such integration remains limited. A key challenge is the discrepancy between medical students’ positive perceptions of AI and their actual competencies, with research in Japan identifying specific gaps in the students’ competencies in understanding regulations and discussing ethical issues.

In the current era of artificial intelligence (AI), utilization of AI has increased in both clinical practice and medical education. Nevertheless, it is probable that perspectives on the prospects and risks of AI vary among individuals. Given the potential for attitudes toward AI to significantly influence its integration into medical practice and educational initiatives, it is essential to assess these attitudes using a validated tool. The recently developed 12-item Attitude towards Artificial Intelligence (ATTARI-12) scale has demonstrated good validity and reliability for the general populations, suggesting its potential for extensive utilization in future studies. However, to our knowledge, there is currently no validated Japanese version of the scale. The lack of a Japanese version hinders research and educational efforts aimed at understanding and improving AI integration into the Japanese healthcare and medical education system.

As medical and allied health curricula adapt to increasing time constraints, ethical considerations, and resource limitations, digital innovations are becoming vital supplements to donor-based anatomy instruction. While prior studies have examined the effectiveness of prosection versus dissection and the role of digital tools in anatomy learning, few resources align interactive digital modules directly with hands-on prosection experiences.

In recent years, generative artificial intelligence and large language models (LLMs) have rapidly advanced, offering significant potential to transform medical education. Several studies have evaluated the performance of chatbots on multiple-choice medical exams.