BACKGROUND: Artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT (OpenAI), is rapidly influencing medical education. Its effectiveness for students with varying levels of prior knowledge remains underexplored.
BACKGROUND: In medical education, mentoring and feedback play crucial roles. Providing feedback on exam performance is a vital component as it allows students to improve. Feedback has to be tailor made and specific to the individual student. This nee...
The rapid evolution of artificial intelligence (AI) and its growing role in clinical settings have made AI education a priority in undergraduate medical education. To support this, AI curricula must align with existing medical education frameworks wh...
Feedback and feedforward are highly relevant in promoting students' learning. With advances in artificial intelligence (AI), new opportunities to support feedback and feedforward are emerging. However, few studies have explored how medical students p...
Large language models (LLMs) are increasingly used in healthcare and medical education, but their performance on institution-authored multiple-choice questions (MCQs), particularly with negative marking, remains unclear. To compare the examination pe...
BACKGROUND: The integration of artificial intelligence (AI) in medical education is evolving, offering new tools to enhance teaching and assessment. Among these, script concordance tests (SCTs) are well-suited to evaluate clinical reasoning in contex...
BACKGROUND: There is a need to increase health care professional training capacity to meet global needs by 2030. Effective communication is essential for delivering safe and effective patient care. Artificial intelligence (AI) technologies may provid...
Pediatric pneumonia (PP) remains an important topic in undergraduate medical education and offers a suitable framework for evaluating large language models (LLMs) in AI-assisted learning. We developed a 27 open-ended survey including five core domain...
BACKGROUND: The irrevocable alteration of medical education due to widespread access to large language models (LLMs) in 2022, and the concomitant surge in AI-related literature, has prompted us to update the evolving impact of AI on undergraduate med...
An April 2025 survey of 118 first-year Japanese medical students found high use of generative artificial intelligence (84.7%) but limited formal learning (49.2%), with strong learning interest yet neutral assignment use, indicating a need for structu...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.