JMIR Medical Education

Artificial intelligence (AI) is increasingly influencing medical education by enabling adaptive learning, AI-assisted assessment, and scalable instructional tools. Natural language processing, machine learning, and generative large language models offer innovative ways to support teaching and learning, yet their integration raises ethical, pedagogical, and infrastructural challenges. This viewpoint article aims to examine the current applications, benefits, and challenges of AI in medical education and propose strategies for responsible and effective integration. AI tools such as chatbots, virtual patients, and intelligent tutoring systems enhance personalized and immersive learning. Automated grading and predictive analytics support efficient evaluations, while AI-assisted writing tools streamline content creation. Despite these advances, concerns persist around data privacy, algorithmic bias, unequal access, and diminished critical thinking. Key solutions include AI literacy training, data oversight, equitable infrastructure, and curriculum reform. The FACETS framework offers six dimensions—Form, Application, Context, Instructional Mode, Technology, and the SAMR model—to evaluate AI integration effectively. AI offers substantial opportunities to transform medical education, but its adoption must be ethical, equitable, and pedagogically grounded. Strategic frameworks such as FACETS, combined with institutional governance and cross-sector collaboration, are essential to guide implementation so that AI enhances learning outcomes while preserving the humanistic foundations of medical practice.

Trust is increasingly recognized as a cornerstone for the successful implementation of digital public health initiatives, from mobile applications to the use of AI in medicine, yet it remains underrepresented in educational curricula. In the course of our research and teaching activities in the field of trust in digital public health and medicine, we identified a gap in existing educational resources aimed at supporting students in conducting structured trust analyses. Digitalization introduces new complexities into trust relationships, as interactions become increasingly mediated by digital tools. Preparing future professionals therefore demands fostering a critical understanding of how trust operates within digital systems, especially in the health sector. To address this gap, we developed and tested the first Trust Analysis Canvas for Teaching (TACT), a tool designed to guide students in conducting trust analyses of case studies in digital public health and medicine. Grounded in conceptual research on trust in health systems and health data sharing, we: (1) developed the canvas content and reviewed it with two trust researchers; (2) tested and iteratively refined the tool with 23 students (3 BSc, 14 MSc, 6 PhD) from diverse disciplines and academic levels through in-person and online focus groups at the Universities of Zurich and Bern; (3) collaborated with a graphic designer to optimize its visual layout; and (4) translated the final canvas into French, Italian, German, and Spanish to ensure accessibility across disciplines, academic levels, and languages while maintaining a clear and engaging visual design. This paper introduces TACT, a canvas comprising 16 guiding questions organized around six core dimensions to support students in conducting trust analyses of case studies in digital public health and medicine. We outline the development process and provide a practical, step-by-step tutorial demonstrating its application through a written trust analysis of a digital health case study, supported by references to relevant literature. TACT is designed to enable students from diverse disciplinary backgrounds and academic levels to engage with the complex concept of trust in a structured and guided manner, thereby addressing the identified gap in current curricula.

Artificial intelligence (AI) is changing continuing professional development (CPD) in healthcare and its interactions with the broader healthcare system. Yet current scholarship lacks an integrated theoretical model that explains how AI impacts CPD as a complex sociotechnical system. Existing frameworks usually focus on isolated phenomena, such as ethics, literacy, or learning theory, leaving unaddressed the dynamics of how those phenomena interact in the complex socio-technical AI-enhanced CPD system, as well as the new roles that AI-empowered patients and society play.

Laparoscopic surgery has a flatter learning curve compared to traditional open surgery. Therefore, structured programs and realistic training models are imperative to ensure patients’ safety. However, commercially available models are often too expensive or technically unrealistic for continuous surgical training.

Depression is a major global health care challenge, causing significant individual distress but also contributing to a substantial global burden. Timely and accurate diagnosis is crucial. To help future clinicians develop these essential skills, we trained a generative pretrained transformer (GPT)–powered chatbot to simulate patients with varying degrees of depression and suicidality.

Beyond its applications in other settings, virtual reality (VR) technology has gained attention in medical education, offering immersive learning experiences. Previous research has demonstrated its potential as an educational tool in medical settings, highlighting enhanced educational outcomes, skill acquisition and retention, standardized training experiences, and the promotion of active learning. However, there is still a dearth of research exploring various aspects of VR user experiences, with most studies focusing on its effect on skill acquisition. Limited qualitative research further hinders an in-depth understanding of user experiences, restricting a comprehensive overview of VR’s potential in medical education.

Healthcare has widely adopted behavioral economics to influence clinical practice, with documented success using defaults and social comparison feedback in electronic health records. Yet online medical education, now the dominant modality for continuing professional development, remains designed on assumptions of rational learning that behavioral science has disproven in clinical contexts. This viewpoint examines the paradox of applying sophisticated behavioral insights to clinical work while designing digital learning environments as if learners are immune to cognitive limitations. We propose digital choice architecture for medical education: intentional integration of behavioral design principles into learning management systems and online platforms. Drawing from clinical nudge units and implementation science, we demonstrate how defaults, social norms, and commitment devices can be systematically applied to digital continuing education. As medical education becomes increasingly technology-mediated, behavioral science provides theoretical foundation and practical tools for designing online learning environments that align with how clinicians actually make decisions.

Artificial intelligence (AI) shows promise in clinical diagnosis, treatment support, and health care efficiency. However, its adoption in real-world practice remains limited due to insufficient clinical validation and an unclear impact on practitioners’ competence. Addressing these gaps is essential for effective, confident, and ethical integration of AI into modern health care settings.

Burnout and psychological distress are prevalent among physicians. Peer support appears to play a protective role, yet little is known about training interventions that motivate physicians to approach peers in difficulty, as such effects are often overlooked or assessed using nonvalidated tools.

The medical education of French family medicine residents involves active, socio-constructivist-inspired small-group courses useful for skill acquisition. This is challenged by the increasing gap between the growing number of residents and the limited number of teachers. Blended courses have the potential to address this issue by reducing the duration of face-to-face sessions while preserving small-group courses.

Physician empathy is important not only for improving patient satisfaction and health outcomes but also for increasing physician job satisfaction and protecting against burnout. Amidst concerns over declining empathy levels in medical education, however, there is a need for innovative teaching approaches that address the empathy gap, a critical element in patient-centered care.

Ultrasound is very important in medicine and teaching, but there are not many formal training programs. We also do not know much about what students think. To be good at using ultrasound, one needs to learn technical, thinking, and seeing skills. This is especially true in regional anesthesia (RA), where mistakes in reading images can cause problems. Training with simulations is a safe and good way to learn these skills. Some models are helpful for teaching how to perform procedures using ultrasound.