JMIR Medical Education

As health care moves to a more digital environment, there is a growing need to train future family doctors on the clinical uses of artificial intelligence (AI). However, family medicine training in AI has often been inconsistent or lacking.

Basic Life Support improves survival prognosis after out-of-hospital cardiac arrest (OHCA) but is too rarely provided before the arrival of professional rescue services. To decrease the delay between collapse and initiation of resuscitation maneuvers, first responder networks have been developed in many regions of the world. Their efficiency depends on the number of first responders available and many networks lack potential rescuers. Medical, dental and biomedical students represent an almost untapped source of potential first responders, and a first study, carried out during the COVID-19 pandemic, led to the recruitment of many of these future professionals even though many restrictions were still in effect.

Nursing students’ motivation in clinical learning is very important not only for their academic and professional achievement but also for making timely, informed, and appropriate decisions in providing quality and cost-effective care to people. However, the increased number of students and the scarcity of medical supplies, equipment, and patients, just to mention a few, have posed a challenge to educators in identifying and navigating the best approaches to motivate nursing students to learn during their clinical placements.

As artificial intelligence and machine learning become increasingly influential in clinical practice, it is critical for future physicians to understand how such novel technologies will impact the delivery of patient care.

Medical education has undergone professionalisation during the last decades and internationally educators are trained in specific medical education courses also known as “train the trainer” courses. As these courses have developed organically based on local needs, lack of a general structure and terminology can confuse and hinder educators’ information and development. The first aim of this study was to conduct a national search, analyse the findings, and provide a presentation of medical education courses based on international theoretical frameworks to support Swiss course providers and educators searching for courses. The second aim was to provide a blueprint for such a procedure to be used by the international audience.

The concept of online learning in medical education has been gaining traction, but whether it can accommodate the complexity of higher-level psychiatric training remains uncertain.

The study by Dzuali and Seiger et al. explores the use of ChatGPT for translating patient education materials into multiple languages, highlighting its potential to bridge gaps in language-concordant care. While the research successfully demonstrates ChatGPT’s ability to provide clinically usable translations for Spanish and Russian, its performance with Mandarin is notably suboptimal due to linguistic complexities, such as nuanced sentence structures and specialized terminology. This raises important considerations for refining AI translation approaches, particularly for languages like Mandarin, where cultural context and grammar intricacies significantly impact translation accuracy. Additionally, the study's reliance on post-translation review by board-certified dermatologists could be enhanced by incorporating a wider range of human oversight, including linguistic experts and specialists in medical translation. Future research should explore the use of alternative prompts and varying levels of human intervention to improve translation quality and ensure culturally appropriate, clinically relevant translations across diverse languages. This work contributes valuable insights into the evolving field of AI-assisted medical translation and highlights areas for further development and validation.

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The motivational design of online instruction is critical in influencing learners’ motivation. Given the multifaceted and situated nature of motivation, educators need access to a range of evidence-based motivational design strategies that target different motivational constructs (eg, interest or confidence).

Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs), have started a new era of innovation across various fields, with medicine at the forefront of this technological revolution. Many studies indicated that at the current level of development, LLMs can pass different board exams. However, the ability to answer specific subject-related questions requires validation.

Appropriate antithrombotic drug use is crucial knowledge to be acquired by pharmacy students.

Letter to editor, abstract is not available