MEDCO: Medical Education Copilots Based on A Multi-Agent Framework

TL;DR

MEDCO introduces a multi-agent system for medical education that simulates real-world training environments, enhancing interactive learning, collaboration, and question-asking skills among students using AI copilots.

Contribution

This work presents the first multi-agent framework for medical education copilots, enabling multi-disciplinary collaboration and interactive training in a simulated environment.

Findings

Simulated students trained with MEDCO showed performance comparable to advanced models.

MEDCO-trained students demonstrated human-like learning behaviors.

Training with MEDCO increased the number of learning samples.

Abstract

Large language models (LLMs) have had a significant impact on diverse research domains, including medicine and healthcare. However, the potential of LLMs as copilots in medical education remains underexplored. Current AI-assisted educational tools are limited by their solitary learning approach and inability to simulate the multi-disciplinary and interactive nature of actual medical training. To address these limitations, we propose MEDCO (Medical EDucation COpilots), a novel multi-agent-based copilot system specially developed to emulate real-world medical training environments. MEDCO incorporates three primary agents: an agentic patient, an expert doctor, and a radiologist, facilitating a multi-modal and interactive learning environment. Our framework emphasizes the learning of proficient question-asking skills, multi-disciplinary collaboration, and peer discussions between students. Our experiments show that simulated virtual students who underwent training with MEDCO not only achieved substantial performance enhancements comparable to those of advanced models, but also demonstrated human-like learning behaviors and improvements, coupled with an increase in the number of learning samples. This work contributes to medical education by introducing a copilot that implements an interactive and collaborative learning approach. It also provides valuable insights into the effectiveness of AI-integrated training paradigms.