AI and Machine Learning in Animal Disease Detection: Benefits, Labor Reduction, and Ethical Considerations

Abstract

AI and ML are revolutionizing veterinary medicine, offering precision diagnostics, early disease detection, and proactive animal care. This review examines the applications of AI-powered technologies in detecting and managing diseases in livestock and companion animals. In recent years, convolutional neural networks (CNNs) such as VGG16 and MobileNetV2 have been shown to have high diagnostic accuracy in certain veterinary imaging and disease-detection applications. These systems can detect subtle pathological patterns and aid in the early diagnosis of diseases like bovine respiratory disease and canine disorders. In addition to imaging, AI-powered wearable and Internet of Things (IoT) devices track physiological and behavioral metrics such as body temperature, heart rate, and movement, allowing for the early detection of health abnormalities. Predictive analytics also combines environmental, animal-movement and historical health data to aid in disease surveillance and outbreak prediction. AI-driven technologies can also enhance the efficiency of veterinary operations by automating image analysis, initial assessment, and decision-making processes, thereby alleviating repetitive tasks. But they come with algorithmic bias, small and unrepresentative datasets, data privacy, model interpretability, and suitable human oversight challenges. Standardized validation, multiple data sets, clear algorithms, secure data governance, and ongoing veterinary oversight are essential for responsible adoption. In summary, AI and ML can be a powerful tool for improving animal health, welfare, diagnostic efficiency, and disease surveillance, provided they are used responsibly.