AI Veterinary Assistance: Enhancing Clinical Decision-Making in Animal Healthcare

Abstract

As the number of households raising companion animals increases, there is a corresponding increase in the demand for veterinary medical services. Furthermore, as companion animals age, the prevalence of chronic disease is increasing, leading companion animal owners to seek more help with long-term health management. However, the shortage of veterinarians and the growing complexity of the consultation process have led to a heavier workload for veterinarians, potentially hindering clinical decision-making efficiency. To address these challenges, we propose AI Veterinary Assistance (AVA), a framework leveraging a large language model. AVA automatically extracts symptoms from consultation records, predicts most probable disease using veterinarian certified disease-symptom database and recommend future consultation question. AVA achieved disease prediction accuracies of 91.4%, 93.4%, and 95.9% for Top-3, Top-5, and Top-10, respectively, along with a symptom extraction accuracy of 79.9% on a consultation records dataset constructed from a veterinarian-certified disease-symptom database. Furthermore, on a real-world dataset, AVA achieved disease prediction accuracies of 38.6%, 43.6%, and 51.5% for Top-3, Top-5, and Top-10, respectively. In both datasets, AVA outperformed baseline methods, demonstrating its effectiveness in supporting clinical decision-making. These results suggest that AVA can help reduce veterinarians’ workload while enhancing the efficiency and reliability of the consultation process.

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