Abstract
Large language models (LLMs) such as ChatGPT have sparked extensive discourse within the medical education community, spurring both excitement and apprehension. Written from the perspective of medical students, this editorial offers insights gleaned through immersive interactions with ChatGPT, contextualized by ongoing research into the imminent role of LLMs in health care. Three distinct positive use cases for ChatGPT were identified: facilitating differential diagnosis brainstorming, providing interactive practice cases, and aiding in multiple-choice question review. These use cases can effectively help students learn foundational medical knowledge during the preclinical curriculum while reinforcing the learning of core Entrustable Professional Activities. Simultaneously, we highlight key limitations of LLMs in medical education, including their insufficient ability to teach the integration of contextual and external information, comprehend sensory and nonverbal cues, cultivate rapport and interpersonal interaction, and align with overarching medical education and patient care goals. Through interacting with LLMs to augment learning during medical school, students can gain an understanding of their strengths and weaknesses. This understanding will be pivotal as we navigate a health care landscape increasingly intertwined with LLMs and artificial intelligence.
Reference41 articles.
1. Introducing ChatGPTOpenAI2023-06-06https://openai.com/blog/chatgpt
2. BrantsTPopatACXuPOchFJDeanJLarge language models in machine translationProceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning2007EMNLP-CoNLLJune 2007Prague858867
3. The NLP Cookbook: Modern Recipes for Transformer Based Deep Learning Architectures
4. Advances in natural language processing
Cited by
15 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献