OBJECTIVE: Large language models (LLMs) have advanced rapidly, but their utility in pediatric surgery remains uncertain. This study assessed the performance of three AI models-DeepSeek, Microsoft Copilot (GPT-4) and Google Bard-on the European Pediat...
BACKGROUND: Optimizing the skill of answering Short answer questions (SAQ) in medical undergraduates with personalized feedback is challenging. With the increasing number of students and staff shortages this task is becoming practically difficult. He...
BACKGROUND: Artificial intelligence and large language models (LLMs)-particularly GPT-4 and GPT-4o-have demonstrated high correct-answer rates in medical examinations. GPT-4o has enhanced diagnostic capabilities, advanced image processing, and update...
OBJECTIVE: This study systematically evaluates the performance of artificial intelligence (AI)-generated examinations in periodontology education, comparing their quality, student outcomes, and practical applications with those of human-designed exam...
Generating high quality MCQs is time consuming and expensive. Many strategies are applied to produce high quality items including sharing of item banks, training of item writers and automatic item generation (AIG). Generative AI, when used with preci...
Large language models (LLMs) have shown promising capabilities across medical disciplines, yet their performance in basic medical sciences remains incompletely characterized. Medical histology, requiring factual knowledge and interpretative skills, p...
BACKGROUNDS: Traditional methods of teaching history-taking in medical education are limited by scalability and resource intensity. This study aims to assess the effectiveness of simulated patient interactions based on a custom-designed Generative Pr...
BACKGROUND: The integration of artificial intelligence (AI) into healthcare is rapidly advancing, with profound implications for medical practice. However, a gap exists in formal AI education for pre-medical students. This study evaluates the effecti...
Comprehensive medical assessments are critical for evaluating clinical proficiency in medical education; however, these administrations impose significant institutional burdens, financial costs, and psychological strain on students. While Artificial ...
The ability of large language models (LLMs) to accurately answer medical board-style questions reflects their potential to benefit medical education and real-time clinical decision-making. With the recent advance to reasoning models, the latest LLMs ...
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