Go to ACL ARR 2024 December homepageRareAgents: Autonomous Multi-disciplinary Team for Rare Disease Diagnosis and Treatment
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. Our code can be found at https://anonymous.4open.science/r/AutoMDT-65EC.
Paper Type: Long
Research Area: NLP Applications
Research Area Keywords: healthcare applications,clinical NLP,biomedical QA
Contribution Types: NLP engineering experiment, Data resources
Languages Studied: English
Submission Number: 534
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