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.

翻译：步态分析是脑卒中后康复的关键环节，但其过程耗时且认知负荷高，尤其在临床医生需将步态视频与动作捕捉数据整合为结构化报告时更为突出。我们提出OGA-AID系统——一种临床医生参与的多智能体大语言模型系统，用于多模态报告草拟。该系统协调三个专用智能体，将患者运动记录、运动学轨迹与临床概况综合生成结构化评估。经康复专家基于真实患者数据验证，OGA-AID在低误差条件下持续优于单次多模态基线模型。在临床医生参与的设置中，简短专家初步笔记相较于参考评估进一步降低了误差。研究结果证实了多模态智能体系统在结构化临床步态评估中的可行性，并揭示了康复工作流程中AI辅助分析与人类临床判断之间的互补关系。