JMIR Medical Education

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.

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.

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.

Large language models (LLMs) have demonstrated significant potential in academic research but face challenges in generating accurate citations. The issue of hallucinated references—well-formatted but fictitious citations—arises due to LLMs' limited access to subscription-based databases and their reliance on probabilistic text generation. This letter discusses two key approaches to mitigating these issues. First, retrieval-augmented generation (RAG) combined with Hallucination Aware Tuning (HAT) improves citation integrity by integrating external databases and employing hallucination detection models. However, even RAG-HAT systems may still misinterpret source content. Second, we propose the development of “Reference-Accurate” Academic LLMs by major global publishers, which would be trained exclusively on rigorously verified academic literature, ensuring that all citations generated are authentic and traceable. We recommend a dual approach integrating RAG-HAT with publisher-backed academic LLMs, along with human oversight, to enhance AI-assisted scholarly communication. Future research should evaluate the accuracy and reliability of these methods to promote responsible AI use in academia.

Letter to editor, abstract is not available

The use of extended reality (XR) technologies in health care can potentially address some of the significant resource and time constraints related to delivering training for health care professionals. While substantial progress in realizing this potential has been made across several domains, including surgery, anatomy, and rehabilitation, the implementation of XR in mental health training, where nuanced humanistic interactions are central, has lagged.

Virtual Reality (VR) has emerged as a promising tool in medical education, particularly for fostering critical skills such as empathy. However, how VR, combined with perspective-taking, influences affective empathy in nursing education remains underexplored.