JMIR Medical Education

The importance of digital health education is widely recognised; however, structural and knowledge deficits hinder its effective integration into training and on-the-job upskilling programmes. Tackling these challenges will equip clinicians to navigate the fast-evolving digital mental health landscape confidently.

Artificial intelligence (AI) is rapidly changing both clinical psychiatry and the education of medical professionals. However, little is currently known about how AI is being discussed in the education and training of psychiatry for medical students and doctors around the world.

While artificial intelligence (AI)–generated feedback offers significant potential to overcome constraints on faculty time and resources associated with providing personalized feedback, its perceived usefulness can be undermined by algorithm aversion. In-context learning, particularly the few-shot approach, has emerged as a promising paradigm for enhancing AI performance. However, there is limited research investigating its usefulness, especially in health profession education.

The integration of artificial intelligence (AI) and machine learning (ML) into biomedical research requires a workforce fluent in both computational methods and clinical applications. Structured, interdisciplinary training opportunities remain limited, creating a gap between data scientists and clinicians. The National Institutes of Health’s Bridge2AI initiative launched the Artificial Intelligence–Ready and Exploratory Atlas for Diabetes Insights (AI-READI) Data Generation Project to address this gap. AI-READI is creating a multimodal, FAIR (Findable, Accessible, Interoperable, and Reusable) dataset—including ophthalmic imaging, physiologic measurements, wearable sensor data, and survey responses—from approximately 4,000 participants with or at risk for type 2 diabetes. In parallel, AI-READI established a yearlong mentored research program that begins with a two-week immersive summer bootcamp to provide foundational AI/ML skills grounded in domain-relevant biomedical data.

Technological innovation is reshaping the landscape of medical education, bringing revolutionary changes to traditional teaching methods. In this context, the upgrade of the teaching model for microscopy, as one of the core skills in medical education, is particularly important. Proficiency in microscope operation not only affects medical students’ pathology diagnosis abilities but also directly impacts the precision of surgical procedures and laboratory analysis skills. However, current microscopy pedagogy faces dual challenges: on one hand, traditional teaching lacks real-time image sharing capabilities, severely limiting the effectiveness of immediate instructor guidance; on the other hand, students find it difficult to independently identify technical flaws in their operations, leading to inefficient skill acquisition. Although whole-slide imaging-based microscopy system technology has partially addressed the issue of image visualization, it cannot replicate the tactile feedback and physical interaction experience of the real world. The breakthrough development of 5G communication technology—with its ultrahigh transmission speed and ultralow latency—provides an innovative solution to this teaching challenge. Leveraging this technological advantage, Tongji University’s biology laboratory has pioneered the deployment of a 5G local area network (LAN)–supported digital interactive microscopy system, creating a new model for microscopy education.

The Japanese National Medical Licensing Examination (NMLE) is mandatory for all medical graduates seeking to become licensed physicians in Japan. Given the cultural emphasis on summative assessment, the NMLE has had a significant impact on Japanese medical education. Although the NMLE Content Guidelines have been revised approximately every five years over the last 2 decades, objective literature analyzing how the examination itself has evolved is absent.

Virtual simulated patients (VSPs) powered by generative artificial intelligence (GAI) offer a promising tool for training clinical interviewing skills; yet, little is known about how different system- and user-level variables shape students’ perceptions of these interactions.

Social media platforms are increasingly integrated into higher education, enabling collaborative, student-centered learning. Yet, few instruments specifically measure students’ satisfaction with these activities across platforms. A brief, valid tool is needed to evaluate perceived quality and guide instructional design in social media–based learning environments.

The rapid transformation of the healthcare landscape requires physicians to not only be skilled clinically but also navigate and lead a highly dynamic, innovation-driven environment. This also provides an avenue for physicians to significantly enhance their ability to help their patients, through participation in health innovation projects. Despite this growing need and opportunity, few medical schools provide formal training in innovation and entrepreneurship (I&E). In this perspective, we examine the need for I&E education in medical curricula by exploring student interest, effective program models, and implementation strategies. To better understand medical student interest in innovation and willingness to participate in I&E programs during medical school, we surveyed 480 medical students at our institution, the Johns Hopkins University School of Medicine (19% response rate). We observed a strong interest in healthcare I&E, with 97% (n = 87) of respondents valuing knowledge or experience in I&E and 63% (n = 56) expressing intent to incorporate I&E into their careers. To assess the real-world impact of I&E education on medical professionals, we surveyed 12 alumni of the Johns Hopkins Center for Bioengineering Innovation and Design (CBID) Master’s program who had also completed medical school. Graduates reported that their experiences cultivated transferable skills—design thinking, interdisciplinary collaboration, and leadership—that shaped their professional trajectories. We propose three models for incorporating I&E education into existing medical curricula—short-term workshops, one-year gap programs, and longitudinal tracks—and discuss their advantages and tradeoffs. Early and structured exposure to I&E education in medical school empowers students to identify unmet clinical needs, collaborate across disciplines, and develop real-world solutions. As the pace of innovation continues to accelerate, integration of I&E education into medical curricula offers a timely opportunity for medical schools to cultivate physician leaders in this space.

Electronic Medical Records (EMR) are a potentially rich source of information on an individual healthcare providers’ clinical activities. These data provide an opportunity to tailor online learning for healthcare providers to align closely with their practice. There is increasing interest in the use of EMR data to understand performance and support continuous and targeted education for healthcare providers.

The evolution of the healthcare landscape necessitates expanding the roles of pharmacists in patient-centered care to encompass direct patient management, collaborative practice, and preventive service. These responsibilities can be fulfilled by pharmacists through ongoing professional development, in which continuing education (CE) is instrumental to career advancement and improved patient care.

Artificial intelligence (AI) is rapidly reshaping medical education, offering new opportunities to personalize learning, enhance research, and streamline administration. The aim of this study is to provide 12 practical, evidence-informed tips by drawing on current literature and real-world examples to guide the integration of AI into medical education, supporting educators across teaching, research, administration, and ethical domains. Key strategies include using adaptive learning platforms to tailor educational content, using AI tools to provide timely feedback, and incorporating AI-generated clinical scenarios in case-based learning. The importance of fostering AI literacy among students is emphasized, as well as utilizing AI-powered tools for efficient literature reviews, data analysis, and manuscript preparation. Administrative applications such as automating routine tasks, supporting strategic planning through data analysis, and enhancing faculty development with AI-driven platforms are also discussed. Ethical considerations are highlighted, with a focus on ensuring transparency, fairness, and accountability in all AI applications. By following these 12 tips, medical educators can leverage the benefits of AI to improve educational outcomes, increase efficiency, and prepare future clinicians for a technology-driven health care environment.