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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.15204 |
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| _version_ | 1866915204948819968 |
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| author | Mairittha, Tittaya Sawanglok, Tanakon Raden, Panuwit Treesuk, Sorrawit |
| author_facet | Mairittha, Tittaya Sawanglok, Tanakon Raden, Panuwit Treesuk, Sorrawit |
| contents | Swine disease surveillance is critical to the sustainability of global agriculture, yet its effectiveness is frequently undermined by limited veterinary resources, delayed identification of cases, and variability in diagnostic accuracy. To overcome these barriers, we introduce a novel AI-powered, multi-agent diagnostic system that leverages Retrieval-Augmented Generation (RAG) to deliver timely, evidence-based disease detection and clinical guidance. By automatically classifying user inputs into either Knowledge Retrieval Queries or Symptom-Based Diagnostic Queries, the system ensures targeted information retrieval and facilitates precise diagnostic reasoning. An adaptive questioning protocol systematically collects relevant clinical signs, while a confidence-weighted decision fusion mechanism integrates multiple diagnostic hypotheses to generate robust disease predictions and treatment recommendations. Comprehensive evaluations encompassing query classification, disease diagnosis, and knowledge retrieval demonstrate that the system achieves high accuracy, rapid response times, and consistent reliability. By providing a scalable, AI-driven diagnostic framework, this approach enhances veterinary decision-making, advances sustainable livestock management practices, and contributes substantively to the realization of global food security. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15204 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | When Pigs Get Sick: Multi-Agent AI for Swine Disease Detection Mairittha, Tittaya Sawanglok, Tanakon Raden, Panuwit Treesuk, Sorrawit Human-Computer Interaction Artificial Intelligence Computation and Language Information Retrieval Multiagent Systems Swine disease surveillance is critical to the sustainability of global agriculture, yet its effectiveness is frequently undermined by limited veterinary resources, delayed identification of cases, and variability in diagnostic accuracy. To overcome these barriers, we introduce a novel AI-powered, multi-agent diagnostic system that leverages Retrieval-Augmented Generation (RAG) to deliver timely, evidence-based disease detection and clinical guidance. By automatically classifying user inputs into either Knowledge Retrieval Queries or Symptom-Based Diagnostic Queries, the system ensures targeted information retrieval and facilitates precise diagnostic reasoning. An adaptive questioning protocol systematically collects relevant clinical signs, while a confidence-weighted decision fusion mechanism integrates multiple diagnostic hypotheses to generate robust disease predictions and treatment recommendations. Comprehensive evaluations encompassing query classification, disease diagnosis, and knowledge retrieval demonstrate that the system achieves high accuracy, rapid response times, and consistent reliability. By providing a scalable, AI-driven diagnostic framework, this approach enhances veterinary decision-making, advances sustainable livestock management practices, and contributes substantively to the realization of global food security. |
| title | When Pigs Get Sick: Multi-Agent AI for Swine Disease Detection |
| topic | Human-Computer Interaction Artificial Intelligence Computation and Language Information Retrieval Multiagent Systems |
| url | https://arxiv.org/abs/2503.15204 |