用多智能体AI提升猪病早期诊断，让兽医更高效精准
When Pigs Get Sick: Multi-Agent AI for Swine Disease Detection
- 分两步识别用户输入：查知识或报症状，定向调取信息
- 自适应提问+可信度加权融合，诊断准确率高且响应快
- 适合兽医、养殖业者使用，助力全球粮食安全
猪病监测对全球农业可持续发展至关重要，但常因兽医资源有限、病例发现延迟及诊断准确性波动而受阻。为此，我们提出一种基于检索增强生成（RAG）的多智能体诊断系统，实现及时、有依据的疾病检测与临床指导。系统可自动将用户输入分类为知识查询或症状诊断查询，确保信息检索精准，并通过自适应提问机制系统性收集临床体征，再以置信度加权融合多个诊断假设，生成稳健的疾病预测与治疗建议。综合评估涵盖查询分类、疾病诊断与知识检索，结果表明该系统具备高精度、快速响应和一致可靠性。该方法提供了一种可扩展的AI驱动诊断框架，提升了兽医决策能力，推动可持续畜牧管理，实质性促进全球粮食安全。
原文摘要 · Abstract (English)
Swine disease surveillance is critical to the sustainability of global agriculture, yet its effectiveness is frequently undermined by limited veterinary resources, delayed identification of cases, and variability in diagnostic accuracy. To overcome these barriers, we introduce a novel AI-powered, multi-agent diagnostic system that leverages Retrieval-Augmented Generation (RAG) to deliver timely, evidence-based disease detection and clinical guidance. By automatically classifying user inputs into either Knowledge Retrieval Queries or Symptom-Based Diagnostic Queries, the system ensures targeted information retrieval and facilitates precise diagnostic reasoning. An adaptive questioning protocol systematically collects relevant clinical signs, while a confidence-weighted decision fusion mechanism integrates multiple diagnostic hypotheses to generate robust disease predictions and treatment recommendations. Comprehensive evaluations encompassing query classification, disease diagnosis, and knowledge retrieval demonstrate that the system achieves high accuracy, rapid response times, and consistent reliability. By providing a scalable, AI-driven diagnostic framework, this approach enhances veterinary decision-making, advances sustainable livestock management practices, and contributes substantively to the realization of global food security.
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