MedAide: Towards an Omni Medical Aide via Specialized LLM-based Multi-Agent Collaboration

Autor:	Wei, Jinjie, Yang, Dingkang, Li, Yanshu, Xu, Qingyao, Chen, Zhaoyu, Li, Mingcheng, Jiang, Yue, Hou, Xiaolu, Zhang, Lihua
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	Large Language Model (LLM)-driven interactive systems currently show potential promise in healthcare domains. Despite their remarkable capabilities, LLMs typically lack personalized recommendations and diagnosis analysis in sophisticated medical applications, causing hallucinations and performance bottlenecks. To address these challenges, this paper proposes MedAide, an LLM-based omni medical multi-agent collaboration framework for specialized healthcare services. Specifically, MedAide first performs query rewriting through retrieval-augmented generation to accomplish accurate medical intent understanding. Immediately, we devise a contextual encoder to obtain intent prototype embeddings, which are used to recognize fine-grained intents by similarity matching. According to the intent relevance, the activated agents collaborate effectively to provide integrated decision analysis. Extensive experiments are conducted on four medical benchmarks with composite intents. Experimental results from automated metrics and expert doctor evaluations show that MedAide outperforms current LLMs and improves their medical proficiency and strategic reasoning. Comment: LLM-based Multi-Agent Collaboration for Medical Applications
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.12532 Zobrazit plný text záznamu View this record from Arxiv