JMIR Medical Education

The release of GPT-4o, an advanced multimodal generative AI model, generated substantial enthusiasm in the field of higher education. However, one year later, medical education continues to face significant challenges, demonstrating the need to move from initial experimentation of the integration of multimodal AI in medical education toward meaningful integration. In this Viewpoint, we argue that GPT-4o’s true value lies not in novelty, but in its potential to enhance training in communication skills, clinical reasoning, and procedural skills by offering real-time simulations and adaptive learning experiences using text, audio, and visual inputs in a safe, immersive and cost-effective environment. We explore how this innovation has made it possible to addresses key medical educational challenges by simulating realistic patient interactions, offering personalised feedback and reducing educator workloads and costs, where traditional teaching methods struggle to replicate the complexity and dynamism of real-world clinical scenarios. However, we also address the critical challenges of this approach, including data accuracy, bias and ethical decision making. Rather than seeing GPT-4o as a replacement, we propose its use as a strategic supplement - scaffolded into curriculum frameworks and evaluated through ongoing research. As the focus shifts from AI novelty to sustainable implementation, we call on educators, policymakers, and curriculum designers to establish governance mechanisms, pilot evaluation strategies, and develop faculty training. The future of AI in medical education depends not on the next breakthrough, but on how we integrate today’s tools with intention and rigour.

Improving the quality of education in clinical settings requires an understanding of learners’ experiences and learning processes. However, this is a significant burden on learners and educators. If learners’ learning records could be automatically analyzed and experiences visualized, it would enable real-time tracking of their progress. Large language models (LLMs) may be useful for this purpose, although their accuracy has not been sufficiently studied.

The integration of digital technologies is becoming increasingly essential in cancer care. However, limited digital health literacy among clinical and nonclinical cancer health care professionals poses significant challenges to effective implementation and sustainability over time. To address this, the European Union is prioritizing the development of targeted digital skills training programs for cancer care providers, the TRANSiTION project among them. A crucial initial step in this effort is conducting a comprehensive gap analysis to identify specific training needs.

ChatGPT is a generative artificial intelligence (AI)-based chatbot developed by OpenAI. Since its release in the second half of 2022, it has been widely applied across various fields. In particular, the application of ChatGPT in medical education has become a significant trend. To gain a comprehensive understanding of the research developments and trends of ChatGPT in medical education, we conducted an extensive review and analysis of the current state of research in this field.

Research capacity building (RCB) among healthcare professionals remains limited, particularly for those working outside academic institutions. Japan experiences a decline in original clinical research due to insufficient RCB infrastructure. Our previous hospital-based workshops showed effectiveness but faced geographical and sustainability constraints. We developed a fully online Scientific Research WorkS Peer Support Group (SRWS-PSG) model that addresses geographical and time-bound constraints and establishes a sustainable economic model. Mentees use online materials, receive support from mentors via a communication platform after formulating their research question, and transition into mentors upon publication.

The integration of large language models (LLMs) into medical education has significantly increased, providing valuable assistance in single-turn, isolated educational tasks. However, their utility remains limited in complex, iterative instructional workflows characteristic of clinical education. Single-prompt AI chatbots lack the necessary contextual awareness and iterative capability required for nuanced educational tasks. This Viewpoint article argues for a shift from conventional chatbot paradigms towards a modular, multi-step AI agent framework that aligns closely with the pedagogical needs of medical educators. We propose a modular framework composed of specialised AI agents, each responsible for distinct instructional subtasks. Furthermore, these agents operate within clearly defined boundaries and are equipped with tools and resources to accomplish their tasks and ensure pedagogical continuity and coherence. Specialised agents enhance accuracy by using models optimally tailored to specific cognitive tasks, increasing the quality of outputs compared to single-model workflows. Using a clinical scenario design as an illustrative example, we demonstrate how task specialisation, iterative feedback, and tool integration in an agent-based pipeline can mirror expert-driven educational processes. The framework maintains a human-in-the-loop structure, with educators reviewing and refining each output before progression, ensuring pedagogical integrity, flexibility, and transparency. Our proposed shift towards modular AI agents offers significant promise for enhancing educational workflows by delegating routine tasks to specialised systems. We encourage educators to explore how these emerging AI ecosystems could transform medical education.

Large language models (LLMs) such as ChatGPT have shown promise in medical education assessments, but the comparative effects of prompt engineering across optimized variants and relative performance against medical students remain unclear.

Training of health professionals and their participation in continuous medical education are crucial to ensure quality health care. Low-resource countries in Sub-Saharan Africa struggle with health care disparities between urban and rural areas concerning access to educational resources. While e-Learning can facilitate a wide distribution of educational content, it depends on learners’ engagement and infrastructure.

Traditional Chinese Medicine (TCM) education in China has evolved significantly, shaped by both national policy and social needs. Despite this, the academic community has yet to fully explore the long-term trends and core issues in TCM education policies. As the global interest in TCM continues to grow, understanding these trends becomes crucial for guiding future policy and educational reforms. This study employs cutting-edge deep learning techniques to fill this gap, offering a novel, data-driven perspective on the evolution of TCM education policies.

Burnout among emergency room health care workers (HCWs) has reached critical levels, affecting up to 43% of HCWs and 35% of emergency medicine personnel during the COVID-19 pandemic. Nurses were most affected, followed by physicians, leading to absenteeism, reduced care quality, and turnover rates as high as 78% in some settings such as Thailand. Beyond workforce instability, burnout compromises patient safety. Each 1-unit increase in emotional exhaustion has been linked to a 2.63-fold rise in reports of poor care quality, 30% increase in patient falls, 47% increase in medication errors, and 32% increase in health care–associated infections. Burnout is also associated with lower job satisfaction, worsening mental health, and increased intent to leave the profession. These findings underscore the urgent need for effective strategies to reduce stress and burnout in emergency care.

This paper aims to describe the co-creation and development processes of an educational ecosystem-centered Bachelor's degree in Digital Health and Biomedical Innovation (SauD InoB). This program is shaped by a multidisciplinary, intersectoral and collaborative framework, involving over 60 organizations in teaching activities and/or internship supervision/hosting, most of which collaborated in needs assessment, curriculum development and public promotion of the degree. In the context of healthcare digital transformation, this comprehensive Bachelor´s degree will respond to unmet demands of the labour market by training students with technological, research and management skills, as well as with basic clinical and biomedical concepts. Graduates will become transdisciplinary, creative professionals capable of understanding and integrating different "languages," and reasoning, clinical processes, and scenarios.

Artificial intelligence (AI) has significantly impacted health care, medicine, and radiology, offering personalized treatment plans, simplified workflows, and informed clinical decisions. ChatGPT (OpenAI), a conversational AI model, has revolutionized health care and medical education by simulating clinical scenarios and improving communication skills. However, inconsistent performance across medical licensing examinations and variability between countries and specialties highlight the need for further research on contextual factors influencing AI accuracy and exploring its potential to enhance technical proficiency and soft skills, making AI a reliable tool in patient care and medical education.