arXiv cs.AI· Sixu Chen, Mingrui Yang, Qiang Guan, Xiaojuan Li·· 9 天前AI 评分22
OA-MAP：面向可解释膝骨关节炎进展评估的证据锚定多智能体多模态框架
OA-MAP: Evidence-Grounded Multi-Agent Multimodal Framework for Interpretable Knee Osteoarthritis Progression
AI 导读
研究团队提出 OA-MAP，一个用于膝骨关节炎（KOA）结构与疼痛进展评估的自主多智能体框架，包含 MRI、X-ray 和临床智能体及一个协调智能体，可自主调度专家智能体、选择工具并检索文献作为外部证据。
正文 · 原文
Abstract:Knee osteoarthritis (KOA) progression prediction can support patient monitoring, requiring the integration of multimodal data and multidomain expertise. Moreover, isolated risk estimates provide limited insight underlying a prediction. To automate the progression assessment workflow and reduce manual effort while providing interpretable findings and supporting evidence, we present OA-MAP, an autonomous multi-agent framework for evidence-grounded assessment of structural and pain progression in KOA. The system incorporates modality-specific agents including MRI, X-ray, and clinical agents, together with a coordinator agent. This framework can autonomously recruit specialist agents, select tools for prediction and analysis, and retrieve literature as external evidence based on user request and available patient information. An uncertainty-informed human-in-the-loop mechanism enables clinicians to review and correct intermediate findings, triggering recomputation of affected results. We evaluate the prediction models using 600 participants from the FNIH Osteoarthritis Biomarkers Consortium cohort. On the test set of 100 participants, the fusion models achieve AUROCs of 0.80 for structural progression and 0.68 for pain progression. A case study illustrates how OA-MAP combines risk estimates with intermediate findings, cross-modal conflicts, literature support, and uncertainty indicators to support interactive review.
| Subjects: | Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2610.12134 [cs.AI] |
| (or arXiv:2610.12134v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12134 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sixu Chen [view email]
[v1]
Thu, 8 Oct 2026 15:23:02 UTC (1,011 KB)
来源：arXiv cs.AI · arxiv.org