JMIR Medical Education

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.

Ultrasound-guided regional anesthesia (UGRA) remains underused in low- and middle-income countries due to barriers to training and equipment. Recent advances in portable ultrasound devices and international partnerships have expanded access to UGRA, enhancing patient safety and quality of care.

Artificial intelligence (AI) literacy is increasingly essential for medical students. However, without systematic characterization of the relevant components, designing targeted medical education interventions may be challenging.

Artificial Intelligence (AI), particularly large language models (LLMs) like ChatGPT, is rapidly influencing medical education. Its effectiveness for students with varying levels of prior knowledge remains underexplored

Podcasts are increasingly used in health professions education, yet most formats are asynchro-nous and non-interactive. Didactically grounded, synchronous implementations in dental cur-ricula are scarce.

Ophthalmology poses distinctive learning challenges for medical students due to its complex anatomy and essential hands-on skills. Problem-based learning (PBL), a student-centered approach, fosters clinical reasoning and self-directed learning. To address the time and logistical constraints of traditional teaching methods, this study implemented a WeChat-based PBL model that leverages the platform’s efficiency and interactivity to enhance student engagement and skill acquisition in ophthalmology.