RareAgents: Advancing Rare Disease Care through LLM-Empowered Multi-disciplinary Team
Journal:
arXiv
Published Date:
Dec 17, 2024
Abstract
Rare diseases, despite their low individual incidence, collectively impact
around 300 million people worldwide due to the vast number of diseases. The
involvement of multiple organs and systems, and the shortage of specialized
doctors with relevant experience make diagnosing and treating rare diseases
more challenging than common diseases. Recently, agents powered by large
language models (LLMs) have demonstrated notable applications across various
domains. In the medical field, some agent methods have outperformed direct
prompts in question-answering tasks from medical examinations. However, current
agent frameworks are not well-adapted to real-world clinical scenarios,
especially those involving the complex demands of rare diseases. To bridge this
gap, we introduce RareAgents, the first LLM-driven multi-disciplinary team
framework designed specifically for the complex clinical context of rare
diseases. RareAgents integrates advanced Multidisciplinary Team (MDT)
coordination, memory mechanisms, and medical tools utilization, leveraging
Llama-3.1-8B/70B as the base model. Experimental results show that RareAgents
outperforms state-of-the-art domain-specific models, GPT-4o, and current agent
frameworks in differential diagnosis and medication recommendation for rare
diseases. Furthermore, we contribute a novel rare disease dataset,
MIMIC-IV-Ext-Rare, to support further advancements in this field.
