Polycystic Ovary Syndrome (PCOS) constitutes a significant public health issue affecting 10% of reproductive-aged women, highlighting the critical importance of developing effective diagnostic tools. Previous machine learning and deep learning detection tools are constrained by their reliance on large-scale labeled data and an lack of interpretability. Although multi-agent systems have demonstrated robust capabilities, the potential of such systems for PCOS detection remains largely unexplored. Existing medical multi-agent frameworks are predominantly designed for general medical tasks, suffering from insufficient domain integration and a lack of specific domain knowledge. To address these challenges, we propose Mapis, the first knowledge-grounded multi-agent framework explicitly designed for guideline-based PCOS diagnosis. Specifically, it built upon the 2023 International Guideline into a structured collaborative workflow that simulates the clinical diagnostic process. It decouples complex diagnostic tasks across specialized agents: a gynecological endocrine agent and a radiology agent collaborative to verify inclusion criteria, while an exclusion agent strictly rules out other causes. Furthermore, we construct a comprehensive PCOS knowledge graph to ensure verifiable, evidence-based decision-making. Extensive experiments on public benchmarks and specialized clinical datasets, benchmarking against nine diverse baselines, demonstrate that Mapis significantly outperforms competitive methods. On the clinical dataset, it surpasses traditional machine learning models by 13.56%, single-agent by 6.55%, and previous medical multi-agent systems by 7.05% in Accuracy.

翻译：多囊卵巢综合征（PCOS）是一个影响10%育龄妇女的重大公共卫生问题，凸显了开发有效诊断工具的至关重要性。以往的机器学习和深度学习检测工具受限于其对大规模标注数据的依赖以及缺乏可解释性。尽管多智能体系统已展现出强大的能力，但此类系统在PCOS检测方面的潜力仍很大程度上未被探索。现有的医疗多智能体框架主要设计用于通用医疗任务，存在领域整合不足和缺乏特定领域知识的问题。为应对这些挑战，我们提出了Mapis，这是首个明确为基于指南的PCOS诊断设计的、以知识为基础的多智能体框架。具体而言，它将2023年国际指南构建成一个结构化的协作工作流，模拟临床诊断过程。该框架将复杂的诊断任务分解到专门的智能体：妇科内分泌智能体和放射学智能体协作验证纳入标准，而排除智能体则严格排除其他病因。此外，我们构建了一个全面的PCOS知识图谱，以确保可验证的、基于证据的决策。在公共基准和专门临床数据集上进行的大量实验，与九种不同基线方法进行对比，表明Mapis显著优于竞争方法。在临床数据集上，其准确率较传统机器学习模型提升13.56%，较单智能体提升6.55%，较先前医疗多智能体系统提升7.05%。