AI-Driven Symptom Analysis in Veterinary Healthcare: A Systematic Review of Machine Learning Techniques for Animal Species, Breed, and Disease Identification

Abstract

Artificial intelligence (AI) has become an essential component of modern veterinary healthcare by enabling automated, accurate, and timely analysis of animal health data for disease diagnosis and clinical decision support. The increasing availability of veterinary medical images, electronic health records, wearable sensor data, behavioural observations, and environmental information has accelerated the adoption of machine learning (ML) and deep learning (DL) techniques for intelligent symptom analysis. This review presents a comprehensive overview of AI-driven approaches for veterinary healthcare, focusing on animal species identification, breed classification, symptom recognition, disease diagnosis, and health monitoring. The paper examines traditional machine learning algorithms, including Decision Trees, Random Forests, Support Vector Machines, and Gradient Boosting methods, alongside advanced deep learning architectures such as Convolutional Neural Networks, Vision Transformers, Long Short-Term Memory networks, and multimodal learning frameworks. Furthermore, the review discusses publicly available veterinary datasets, feature engineering techniques, data preprocessing methods, and recent applications across livestock, companion animals, poultry, and wildlife. Current challenges related to limited annotated datasets, interspecies variability, model interpretability, computational complexity, and real-world deployment are critically analysed. Emerging research directions, including explainable artificial intelligence, federated learning, foundation models, edge AI, and multimodal veterinary decision-support systems, are also highlighted as promising solutions for next-generation intelligent veterinary healthcare. The review concludes that AI has significant potential to improve diagnostic accuracy, enable early disease detection, enhance precision livestock farming, and support evidence-based veterinary practice. By consolidating recent advances, existing challenges, and future research opportunities, this review provides a valuable reference for researchers, veterinarians, and developers working toward reliable, scalable, and clinically deployable AI solutions for animal health management and the broader One Health ecosystem.