Application of artificial intelligence in cattle disease diagnosis: A comprehensive review

Cattle diseases severely threaten the global agricultural economy. Traditional disease management, heavily reliant on manual observation, is inherently delayed and subjective. Artificial intelligence (AI) is driving a profound paradigm shift in veterinary diagnostics. By enabling proactive and scalable health monitoring, AI effectively mitigates the economic losses associated with delayed interventions. However, existing reviews primarily focus on individual diseases or specific scenarios, lacking a systematic analysis of cross-disease applications and technological evolution. Consequently, this paper provides a comprehensive review of AI applications in cattle disease diagnosis, with a focus on technological evolution, key challenges, and future directions. A systematic search across four databases (Web of Science, PubMed, Scopus, and EBSCOhost) yielded 316 studies for bibliometric and thematic analysis. Grounded in pathophysiological systems and veterinary clinical practice, the literature is systematically categorized into six core domains: locomotor and superficial anomalies, reproductive and lactation diseases, metabolic and digestive disorders, respiratory conditions, infectious disease prevention, and non-specific health monitoring. The analysis reveals a clear methodological transition from early contact-based sensing and traditional machine learning to non-contact techniques centered on computer vision and deep learning, with current advances toward multimodal data fusion. Vision-based methods are primarily applied to diseases with clear phenotypic traits, whereas multimodal approaches integrating physiological, behavioral, and environmental data demonstrate superior performance in identifying complex and subclinical conditions. Despite these advancements, real-world applications remain constrained by high data heterogeneity, insufficient standardization, multimodal alignment challenges, limited model interpretability, and high deployment costs. Furthermore, practical deployment is also constrained by ethical considerations and environmental complexity. In summary, AI provides valuable technical tools for the early identification, risk assessment, and intervention of cattle diseases. Future research should prioritize multimodal fusion and cloud-edge-device collaborative architectures, while improving deployment efficiency in complex environments through hierarchical computing, real-time decision-making, and advances in explainable AI.