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Large language model-supported interactive case-based learning: a pilot study.

Haelynn Gim¹, Benjamin Cook², Jasmin Le²

¹Harvard Medical School, Boston, Massachusetts, USA.

Internal Medicine Journal

|March 24, 2025

Summary

This summary is machine-generated.

Large language models (LLMs) show promise for case-based learning, with a new tool adhering to medical screenplays in 97.1% of cases. Further research is needed to confirm their educational impact.

Keywords:

artificial intelligence clinical reasoning machine learning medical education natural language processing

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Area of Science:

Medical Education
Artificial Intelligence

Background:

Large language models (LLMs) offer potential for enhancing case-based learning.
A key challenge is LLMs' tendency to generate factually incorrect information.

Purpose of the Study:

To develop and evaluate an LLM-based tool for augmenting case-based learning.
To assess the factual accuracy and medical appropriateness of LLM responses.

Main Methods:

An LLM-based tool was developed for case-based learning.
The tool's performance was evaluated based on adherence to a provided screenplay.
Medical appropriateness of responses was also assessed.

Main Results:

The LLM adhered to the provided screenplay in 97.1% (832/857) of instances.
In remaining instances, responses were medically appropriate in 96.0% (24/25) of cases.

Conclusions:

LLM use appears feasible for augmenting case-based learning.
Further studies are necessary to determine the educational impact of LLMs in this context.