Exploring Generative Artificial Intelligence-Assisted Medical Education: Assessing Case-Based Learning for Medical Students

Abstract

The recent public release of generative artificial intelligence (GenAI) has brought fresh excitement by making access to GenAI for medical education easier than ever before. It is now incumbent upon both students and faculty to determine the optimal role of GenAI within the medical school curriculum. Given the promise and limitations of GenAI, this study aims to assess the current capabilities of a GenAI (Chat Generative Pre-trained Transformer, ChatGPT), specifically within the framework of a pre-clerkship case-based active learning curriculum. The role of GenAI is explored by evaluating its performance in generating educational materials, creating medical assessment questions, answering medical queries, and engaging in clinical reasoning by prompting it to respond to a problem-based learning scenario. Our results demonstrated that GenAI addressed epidemiology, diagnosis, and treatment questions well. However, there were still instances where it failed to provide comprehensive answers. Responses from GenAI might offer essential information, hint at the need for further inquiry, or sometimes omit critical details. GenAI struggled with generating information on complex topics, raising a significant concern when using it as a 'search engine' for medical student queries. This creates uncertainty for students regarding potentially missed critical information. With the increasing integration of GenAI into medical education, it is imperative for faculty to become well-versed in both its advantages and limitations. This awareness will enable them to educate students on using GenAI effectively in medical education.

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