JMIR Publications

ABSTRACT

Large Language Models (LLMs), such as Chat Generative Pre-trained Transformer (ChatGPT), have sparked extensive discourse within the medical education community, spurring both excitement and apprehension. Written from the perspective of medical students, this autoethnographic study offers insights gleaned through immersive interaction with ChatGPT, contextualized by ongoing research into the imminent role of LLMs' in healthcare. 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 non-verbal 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 healthcare landscape increasingly intertwined with LLMs and artificial intelligence.