Summary of Rareagents: Advancing Rare Disease Care Through Llm-empowered Multi-disciplinary Team, by Xuanzhong Chen et al.
RareAgents: Advancing Rare Disease Care through LLM-Empowered Multi-disciplinary Team
by Xuanzhong Chen, Ye Jin, Xiaohao Mao, Lun Wang, Shuyang Zhang, Ting Chen
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces RareAgents, the first large language model-driven framework designed specifically for diagnosing and treating rare diseases. The framework 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 and current agent frameworks in differential diagnosis and medication recommendation for rare diseases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Rare diseases affect millions of people worldwide, but diagnosing and treating them is challenging due to their complexity and the shortage of specialized doctors. Researchers have been exploring ways to use artificial intelligence (AI) to help diagnose and treat these diseases. One type of AI is called a large language model (LLM), which can process natural language and generate responses. LLMs have been used in medical settings before, but this paper introduces RareAgents, the first framework that uses an LLM specifically for diagnosing and treating rare diseases. The framework combines multiple features to help doctors make more accurate diagnoses and recommend better treatments. In tests, RareAgents performed better than other methods. |
Keywords
» Artificial intelligence » Large language model » Llama
