JMIR Medical Education

Medical education has shifted from an individual, teacher-led process to an interactive, group-oriented approach, fostering clinical reasoning and teamwork. Sense of Community (SoC) appears to be a key factor in this process; however, its link to collaborative learning and academic success is underexplored.

Artificial intelligence (AI), particularly generative AI and large language models, is increasingly used for assessment-related tasks in medical education. Existing overviews often address AI in medical education broadly, limiting assessment-specific interpretation of functions, settings, learner stages, and responsible-AI reporting domains.

Ultrasound education has traditionally relied on on-site training, but scalable digital solutions are increasingly needed.

Operating room (OR)-to-intensive care unit (ICU) handoffs are among the most complex and high-risk communication events in perioperative care. Despite the implementation of structured checklists, trainees often receive limited feedback on their communication skills, and simulation-based education rarely provides objective data on communication performance and checklist adherence. This study explores how an ambient artificial intelligence (AI) handoff assistant used during simulation-based training of OR-to-ICU handoff discussions can enhance clinical communication training and AI literacy by mapping spoken handoff discussions to handoff checklist items, providing immediate feedback on checklist item omissions, and generating a structured handoff note that functions as a feedback-rich learning artifact.

Prerecorded courses are increasingly used in medical education, and audio quality is known to influence learners’ comprehension and engagement. Traditional audio recording, however, is time-consuming and may be uncomfortable for some educators. Advances in generative artificial intelligence (AI) now allow for realistic voice cloning, but its pedagogical value compared with conventional recording has not been assessed.

Residency and fellowship are demanding phases characterized by intense schedules, limited autonomy, sleep deprivation, and hierarchical environments. Training years often coincide with peak reproductive age, presenting trainees with the dilemma of delaying parenthood or managing training and parenthood concurrently.

Peer teaching is an established pedagogical approach in medical education; yet, traditional methods face challenges including inconsistent knowledge support, variable teaching quality, and limited scalability. Artificial intelligence (AI) large language models offer potential to augment peer teaching by providing on-demand access to medical knowledge and clinical reasoning support. However, AI integration within structured peer teaching has not been systematically evaluated in clinical education.

To address care delivery gaps, the health care system must embrace innovative digital solutions. Additionally, the rising integration of digitalization as a topic into medical education is providing students with broader opportunities to engage with digitalization overall. As digital health becomes an increasingly integral component of medical education and health care practice, digitally affine early career medical professionals constitute a vital resource for advancing digitalization within the health care sector.

The number of 1-year Accreditation Council for Graduate Medical Education (ACGME) fellowships continues to grow. The ACGME recommends a holistic curriculum with nonclinical areas, inclusive of educational sessions. Given the competing demands between clinical skill development, educational pursuits, and work-hour restrictions, we propose an andragogic curriculum using pediatric anesthesiology as the model fellowship.

Artificial intelligence (AI) is increasingly being used in many aspects of society, including health care and education. AI has the potential to enhance health care delivery, education, and administration. Health care trainees will be required to master these AI technologies. To teach trainees to effectively and ethically leverage AI technologies, educators must be appropriately trained and empowered to use these technologies.

Despite the importance of quality improvement in advancing patient care and safety, there is limited literature describing structured, practical, and co-designed quality improvement education.

In 2021, the American Academy of Pediatrics released a policy statement spotlighting the health-promoting and stress-buffering effects of early relational health (ERH) and calling for universal ERH promotion in pediatric primary care. However, little educational content for the observation and promotion of ERH is available, highlighting the need for scalable ERH training modules.