AI助手融合视频与运动数据，自动生成中风康复步态报告
OGA-AID: Clinician-in-the-loop AI Report Drafting Assistant for Multimodal Observational Gait Analysis in Post-Stroke Rehabilitation
- 多智能体协作分析步态视频与运动轨迹
- 专家微调后错误率低于基线模型
- 适合康复科医生快速生成结构化报告
步态分析对中风康复至关重要，但整合步态视频与运动捕捉数据撰写报告耗时且费力。我们提出OGA-AID，一种临床医生参与的多智能体大语言模型系统，通过三个专用智能体协同处理患者运动记录、运动轨迹和临床信息，生成结构化评估报告。在真实患者数据上由专业理疗师评估，OGA-AID在单次生成中表现优于传统多模态基线，误差低；在医生参与的闭环设置中，仅需简要初步笔记即可进一步降低误差，优于参考评估。结果表明，多模态智能体系统在结构化临床步态评估中可行，凸显了AI辅助分析与人类临床判断在康复流程中的互补性。
原文摘要 · Abstract (English)
Gait analysis is essential in post-stroke rehabilitation but remains time-intensive and cognitively demanding, especially when clinicians must integrate gait videos and motion-capture data into structured reports. We present OGA-AID, a clinician-in-the-loop multi-agent large language model system for multimodal report drafting. The system coordinates 3 specialized agents to synthesize patient movement recordings, kinematic trajectories, and clinical profiles into structured assessments. Evaluated with expert physiotherapists on real patient data, OGA-AID consistently outperforms single-pass multimodal baselines with low error. In clinician-in-the-loop settings, brief expert preliminary notes further reduce error compared to reference assessments. Our findings demonstrate the feasibility of multimodal agentic systems for structured clinical gait assessment and highlight the complementary relationship between AI-assisted analysis and human clinical judgment in rehabilitation workflows.
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