OGA-AID: Clinician-in-the-loop AI Report Drafting Assistant for Multimodal Observational Gait Analysis in Post-Stroke Rehabilitation
This addresses the challenge for clinicians in post-stroke rehabilitation by providing an AI-assisted tool to streamline report drafting, though it appears incremental as it builds on existing multimodal and agentic approaches.
The paper tackled the problem of time-intensive and cognitively demanding gait analysis in post-stroke rehabilitation by developing OGA-AID, a clinician-in-the-loop multi-agent LLM system that synthesizes multimodal data into structured reports, which outperformed baselines with low error and further reduced error with expert input.
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.