Integrating biomarkers and artificial intelligence for precision diagnostics in cattle health and herd management: a review

Abstract Early and accurate diagnosis of major production diseases in cattle, many of which may be subclinical, such as mastitis, ketosis, and bovine respiratory disease (BRD), is essential for herd efficiency, animal welfare, and long-term sustainability. Conventional diagnostic approaches based on clinical observation and microbiological assays often lack sensitivity for early-stage detection and show limited predictive value in multifactorial conditions, thereby limiting early risk stratification and timely intervention. Biomarkers derived from accessible matrices such as blood and milk provide valuable insight into inflammatory, metabolic, and reproductive disturbances, especially at subclinical stages. However, their clinical implementation remains limited by pre-analytical and analytical variability (e.g., sample collection, storage, and processing), the absence of standardized thresholds, and the lack of reliable cow-specific decision cut-offs. The integration of multi-omics data, including genomic, transcriptomic, proteomic, and metabolomic layers, with artificial intelligence (AI) enables high-dimensional data integration, automated classification, and predictive modeling in cattle health. While AI-driven approaches show promise in supporting biomarker network interpretation, their translation from predictive modeling frameworks to clinically validated diagnostic systems remains limited. This review critically synthesizes current evidence on the utility and limitations of biomarkers and AI-assisted diagnostics in cattle health management, while identifying key methodological constraints and outlining practical pathways toward standardized and interpretable AI-driven systems.