BACKGROUND: As large language models (LLMs) evolve, assessing their competence in ethically sensitive domains such as medical ethics has become increasingly important. Since medical ethics is a universal component of medical education, disparities in...
BACKGROUND: The Objective Structured Clinical Examination (OSCE) has been widely used to evaluate students in medical education. However, it is resource-intensive, presenting challenges in implementation. We hypothesized that generative artificial in...
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...
BACKGROUND: Large language models (LLMs) are increasingly used in medical education for feedback and grading; yet their role in postgraduate examination preparation remains uncertain due to inconsistent grading, hallucinations, and user acceptance.
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...
The rapid advancement of artificial intelligence (AI) chatbots has generated significant interest regarding their potential applications within medical education. This study sought to assess the performance of the open-source large language model Dee...
BACKGROUND: Generative artificial intelligence (GenAI), exemplified by ChatGPT and DeepSeek, is rapidly advancing and reshaping human-computer interaction with its growing reasoning capabilities and broad applications across fields such as medicine a...
BACKGROUND: This study explored the diagnostic accuracy of artificial intelligence (AI) chatbots and dental students when responding to questions related to pulpal and periapical diseases. Rapid advancements in AI have led to increased interest in th...
The integration of Artificial Intelligence (AI) in medical education is rapidly transforming assessment practices, offering unprecedented opportunities to enhance student evaluation, feedback, and learning pathways. However, despite the potential, a ...
BACKGROUND: Early identification of students at academic risk is critical in health sciences education, particularly in regions prioritizing healthcare workforce development. This study evaluated the application of established machine learning (ML) c...
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