Can AI agents diagnose swine diseases faster?
WHEN PIGS GET SICK: MULTI-AGENT AI FOR SWINE DISEASE DETECTION
This paper introduces a multi-agent AI system for diagnosing swine diseases. It uses a retrieval-augmented generation (RAG) approach to provide timely and evidence-based diagnoses and treatment recommendations. The system classifies user queries, gathers symptoms via an adaptive questioning protocol, uses multiple specialized diagnostic agents, and fuses their predictions with a confidence-weighted mechanism.
Key LLM-based multi-agent aspects include: specialized agents for different diseases, RAG for retrieving relevant information from a veterinary knowledge base, adaptive questioning to refine diagnostic accuracy, and an iterative refinement process for generating robust recommendations. The system demonstrates the potential of multi-agent architectures and LLMs for complex decision-making in veterinary diagnostics.