This paper explores the potential of LLMs, like ChatGPT and Bard, in revolutionising medical education. Trained on vast medical datasets, these AI models can answer questions, explain complex concepts, and even generate exams. Studies show promising performance on medical licensing exams, highlighting their potential as valuable learning tools. The paper discusses how LLMs can support students by simplifying concepts, simulating patient interactions, and personalising learning. Additionally, it explores how LLMs can streamline exam creation for educators. However, limitations exist, including the inability to handle complex reasoning and the risk of perpetuating errors from training data. Overall, the paper argues that LLMs, despite limitations, are a powerful tool for the future of medical education, offering interactive learning experiences and personalised support for future healthcare professionals.
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Implications of Large Language Models in Medical Education
Semantic Scholar · 2024
Abstract
This paper explores the potential of LLMs, like ChatGPT and Bard, in revolutionising medical education. Trained on vast medical datasets, these AI models can answer questions, explain complex concepts, and even generate exams. Studies show promising performance on medical licensing exams, highlighting their potential as valuable learning tools. The paper discusses how LLMs can support students by simplifying concepts, simulating patient interactions, and personalising learning. Additionally, it explores how LLMs can streamline exam creation for educators. However, limitations exist, including the inability to handle complex reasoning and the risk of perpetuating errors from training data. Overall, the paper argues that LLMs, despite limitations, are a powerful tool for the future of medical education, offering interactive learning experiences and personalised support for future healthcare professionals.