Prospects of using artificial intelligence in productive veterinary medicine

Modern agriculture is increasingly turning to new technologies to improve efficiency, animal welfare and product quality. Veterinary medicine is therefore experiencing a paradigm shift through the use of artificial intelligence (AI) and machine learning in disease prediction and livestock monitoring. The key challenge remains managing farm data and using it effectively. This pilot project will be carried out on commercial dairy farms located in the V4 countries and will be based on a combination of routinely collected data from milking systems (milk yield, electrical conductivity, production and market value index - SPP) with monthly laboratory reports (somatic cell count - SCC, fat, protein, lactose). Of particular importance is the ability to predict mastitis, one of the top three diseases in cows, which causes significant economic losses for farmers through reduced milk production and quality, as well as premature culling. Such AI-based mastitis prediction is also in line with recent efforts to improve animal welfare, reduce production losses, and make antimicrobial use more targeted and responsible. Synthesizing large amounts of farm data into an AI model will enable a shift towards predictive herd management, improve animal welfare, minimize production losses, and facilitate timely, empirical veterinary interventions. The project is expected to start at TRL4 after validation on a real farm, delivering a fully scalable, AI-based decision-support system aligned with the goals of value retention and sustainable livestock production.