Anat Biol Anthropol.  2024 Jun;37(2):59-67. 10.11637/aba.2024.37.2.59.

Reflections on the Development of Large Language Models and Their Impact on Basic Medical Education

• Lee H

• KMID: 2562479
• DOI: http://doi.org/10.11637/aba.2024.37.2.59

Abstract

Rapid advances in artificial intelligence, particularly in natural language processing, are transforming medical education. Large language models (LLMs), which are capable of processing vast amounts of text and generating coherent, contextualized text, offer an unprecedented opportunity to enhance teaching and learning in medicine. This paper reviews the educational potential of LLMs and explores the potential challenges associated with their integration and use. LLMs can facilitate personalized learning experiences and support research by broadening access to a wide range of medical knowledge and streamlining literature reviews. They can also provide a dynamic and immersive educational experience through interactive learning environments that can mimic clinical settings. However, the integration of LLMs in medical education is not without its concerns. It is important to consider the accuracy and reliability of the information provided, the ethical implications, and the over-reliance on technology, which may affect the development of critical thinking skills. Therefore, the integration of LLMs into medical curricula must be approached with careful consideration, ensuring a harmonious balance between cutting-edge technology and traditional learning methodologies. To achieve this, this paper presents guidelines that emphasize the importance of adopting LLMs based on evidence-based reviews, fostering multidisciplinary collaboration, ensuring transparency, and effectively managing cognitive load. These recommendations aim to facilitate a discourse on the effective integration of LLMs into medical education, ultimately helping to prepare healthcare professionals for the challenges and opportunities of the digital age.

Keyword

Large language models; Medical education technology; Artificial intelligence in healthcare; Ethical implications of AI; Cognitive load