Emerging role of artificial intelligence in animal disease surveillance, prediction and diagnosis: A Review

Abstract

Artificial Intelligence (AI) and its subfields Machine Learning (ML), Deep Learning (DL), Computer Vision (CV), and natural language or sound processing are transforming animal health by enabling faster, earlier, and more objective disease prediction and diagnosis. Applications range from sensor- and camera-based early-warning systems in dairy herds to image-based detection of skin and internal lesions, sound analysis for respiratory and behavioural disorders in poultry and swine, and omics-based predictive models for susceptibility and outbreak forecasting. This review summarizes AI methodologies applied in veterinary sciences, key data sources and sensors, notable use-cases (mastitis, lameness, avian influenza, porcine welfare signals, zoonoses), challenges (data quality, bias, interpretability, deployment), and future directions, including federated learning, multimodal integration, and One Health approaches.