RareAgents: Autonomous Multi-disciplinary Team for Rare Disease Diagnosis and Treatment

• URL: http://arxiv.org/abs/2412.12475v1
• Date: Tue, 17 Dec 2024 02:22:24 GMT
• Title: RareAgents: Autonomous Multi-disciplinary Team for Rare Disease Diagnosis and Treatment
• Authors: Xuanzhong Chen, Ye Jin, Xiaohao Mao, Lun Wang, Shuyang Zhang, Ting Chen,
• Abstract summary: Rare diseases collectively impact around 300 million people worldwide due to the huge number of diseases.<n>Recently, agents powered by large language models (LLMs) have demonstrated notable improvements across various domains.<n>RareAgents integrates advanced planning capabilities, memory mechanisms, and medical tools utilization, leveraging Llama-3.1-8B/70B as the base model.
• Score: 13.330661181655493
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rare diseases, despite their low individual incidence, collectively impact around 300 million people worldwide due to the huge number of diseases. The complexity of symptoms 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 improvements across various domains. In the medical field, some agent methods have outperformed direct prompts in question-answering tasks from medical exams. However, current agent frameworks lack adaptation for real-world clinical scenarios, especially those involving the intricate demands of rare diseases. To address these challenges, we present RareAgents, the first multi-disciplinary team of LLM-based agents tailored to the complex clinical context of rare diseases. RareAgents integrates advanced planning capabilities, memory mechanisms, and medical tools utilization, leveraging Llama-3.1-8B/70B as the base model. Experimental results show that RareAgents surpasses state-of-the-art domain-specific models, GPT-4o, and existing agent frameworks in both differential diagnosis and medication recommendation for rare diseases. Furthermore, we contribute a novel dataset, MIMIC-IV-Ext-Rare, derived from MIMIC-IV, to support further advancements in this field.

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