CaseMaster: Designing and Evaluating a Probe for Oral Case Presentation Training with LLM Assistance

TL;DR

This paper introduces CaseMaster, an LLM-based tool designed to improve medical students' oral case presentation skills by providing tailored guidance, showing potential to enhance presentation quality and reduce workload.

Contribution

We developed and evaluated CaseMaster, an interactive LLM-powered probe that supports medical education and offers guidelines for integrating AI tools into training.

Findings

CaseMaster improved presentation quality in a controlled study.

Participants reported reduced workload using CaseMaster.

Expert feedback supported the tool's effectiveness and usability.

Abstract

Preparing an oral case presentation (OCP) is a crucial skill for medical students, requiring clear communication of patient information, clinical findings, and treatment plans. However, inconsistent student participation and limited guidance can make this task challenging. While Large Language Models (LLMs) can provide structured content to streamline the process, their role in facilitating skill development and supporting medical education integration remains underexplored. To address this, we conducted a formative study with six medical educators and developed CaseMaster, an interactive probe that leverages LLM-generated content tailored to medical education to help users enhance their OCP skills. The controlled study suggests CaseMaster has the potential to both improve presentation quality and reduce workload compared to traditional methods, an implication reinforced by expert feedback. We propose guidelines for educators to develop adaptive, user-centered training methods using LLMs, while considering the implications of integrating advanced technologies into medical education.