The concept of utilizing Large Language Models for enhancing case-based learning in medical education

Abstract

Objective. The study is devoted to modern forms of case method implementation as a key tool in the development of clinical thinking of doctors. The main factors complicating the creation of situational tasks and limiting the large-scale application of this method in medical education are identified.

Materials and methods. The concept of using large language models (LLM) to reduce the complexity and labor intensity of developing situational tasks in medical education is proposed.

Results. A prototype of an interactive LLM ChatGPT-4o-based case study based on a clinical guideline for chronic heart failure has been developed and tested. The prototype allows dialogic interaction with learners, generation of laboratory and instrumental data, and real-time adaptation of case complexity. Despite its effectiveness, risks associated with the occurrence of content generation errors (so-called “hallucinations”) have been confirmed.

Conclusion. The concept of LLM application for automation and improvement of case method in medical education is proposed. Requirements for the development of a digital solution are formulated, which will greatly simplify the creation and modification of case problems and will ensure the development of clinical thinking of physicians. Further efforts should be aimed at minimizing generative errors and creating specialized interfaces for effective use of LLM in training.