Health management in the era of artificial intelligence: challenges and opportunities in dairy production

Abstract

The rapid advancement of Artificial Intelligence (AI) is transforming health management practices across various industries, including dairy production. This paper examines how AI-driven tools and technologies can improve animal health monitoring, disease prevention, and overall production efficiency. Specifically, it proposes the development and use of a database derived from the Official Milk Production Control system, updated at 28-day intervals, to collect and analyze key health and production data from dairy cows. The system would use AI algorithms to analyze data patterns such as changes in milk production levels, variations in milk composition (e.g., fat, protein, and fat-to-protein ratio), somatic cell count (SCC), and milk conductivity trends. It would also consider factors like days in milk (DIM) and the lactation curve to provide a comprehensive health assessment. By correlating these parameters, the system could detect early warning signs of diseases—such as mastitis—and generate accurate predictions of potential health risks. Diagnostic reports, trend analyses, and prognoses could then be automatically compiled into easy-to-interpret graphs and summaries, which would be sent to veterinarians, farm managers, and owners for timely intervention. In addition, the paper explores critical challenges associated with implementing AI in production medicine, including ensuring data accuracy, addressing ethical considerations around data usage, and providing adequate training for farmers to effectively utilize these technologies. Through real-world examples from dairy farming operations, this study highlights AI's potential to revolutionize livestock health management while emphasizing the practical and ethical considerations necessary for successful adoption.