DDO: Dual-Decision Optimization via Multi-Agent Collaboration for LLM-Based Medical Consultation

• URL: http://arxiv.org/abs/2505.18630v1
• Date: Sat, 24 May 2025 10:26:57 GMT
• Title: DDO: Dual-Decision Optimization via Multi-Agent Collaboration for LLM-Based Medical Consultation
• Authors: Zhihao Jia, Mingyi Jia, Junwen Duan, Jianxin Wang,
• Abstract summary: Large Language Models (LLMs) demonstrate strong generalization and reasoning abilities.<n>We propose textbfDDO, a novel framework that performs textbfDual-textbfDecision textbfOptimization by decoupling and independently optimizing the the two sub-tasks.
• Score: 10.348275814202848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) demonstrate strong generalization and reasoning abilities, making them well-suited for complex decision-making tasks such as medical consultation (MC). However, existing LLM-based methods often fail to capture the dual nature of MC, which entails two distinct sub-tasks: symptom inquiry, a sequential decision-making process, and disease diagnosis, a classification problem. This mismatch often results in ineffective symptom inquiry and unreliable disease diagnosis. To address this, we propose \textbf{DDO}, a novel LLM-based framework that performs \textbf{D}ual-\textbf{D}ecision \textbf{O}ptimization by decoupling and independently optimizing the the two sub-tasks through a collaborative multi-agent workflow. Experiments on three real-world MC datasets show that DDO consistently outperforms existing LLM-based approaches and achieves competitive performance with state-of-the-art generation-based methods, demonstrating its effectiveness in the MC task.

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