Researchers have developed VetClaw, a multimodal agentic system designed for early veterinary disease screening that operates across edge and cloud environments. The system utilizes a camera on an edge device to capture images and symptom descriptions, which are then processed by a server-hosted vision-language model for zero-shot disease classification. VetClaw separates agent interaction, managed by OpenClaw for scheduling and user services on the edge, from workflow orchestration, handled by LangGraph for state management, model invocation, and safety checks. Initial results indicate that multimodal inputs significantly improve classification performance compared to image-only predictions, transforming the system into a coordinated, safety-aware diagnostic tool. AI
IMPACT This system demonstrates a novel approach to integrating edge and cloud AI for specialized diagnostic tasks, potentially influencing the development of similar agentic systems in other fields.
RANK_REASON The item describes a new research system and its technical details, fitting the research bucket. [lever_c_demoted from research: ic=1 ai=1.0]
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