ARTIFICIAL INTELLIGENCE IN ZOOLOGY: USES IN ANIMAL HEALTH MONITORING AND DISEASE DIAGNOSIS

Abstract

Artificial Intelligence (AI) is revolutionizing veterinary medicine by providing next-generation and scalable technologies for animal health surveillance and diagnostics. Conventional diagnostic techniques, relying on manual observation, laboratory analysis, and interpretation by experts, can be slow, subjective, and ineffective to deal with large data sets. By contrast, AI-based approaches, such as those involving machine and deep learning, computer vision, and sensor technologies, allow for fast, automatic and precise detection of diseases in animals in different settings. This review gives an overall synthesis of the latest advancements in the use of AI in zoology, including livestock, poultry, companion animals, and wildlife health monitoring. It critically analyzes several important methodologies, such as convolutional neural networks, predictive modeling techniques, and Internet of Things (IoT) enabled systems, their diagnostic features, and their practical implementation. The results emphasize that AI technologies play a critical role in the early detection of diseases, the possibility of real-time monitoring, and the process of decision-making and can ultimately improve the welfare of animals and economic losses in animal production systems. Regardless of these innovations, there are a number of obstacles that still restrict the extensive use of AI in veterinary practice. These are the lack of high-quality annotated datasets, low interpretability of complicated models, ethical issues of data use and animal welfare, and constraints on infrastructures in resource-limited environments. These problems need to be tackled to guarantee the reliability and scalability of AI-based solutions. Moreover, the growing trends of explainable AI, multimodal data integration, and real-time monitoring systems are seen as the potential future research directions. On the whole, this review highlights the high potential of AI to transform animal healthcare and the need to work interdisciplinary, develop standardized data formats, and create user-friendly technologies to implement changes in a sustainable manner.