The Application of Artificial Intelligence in Brucellosis: Diagnosis, Genomic Analysis, Epidemiological Prediction and Drug Discovery

Brucellosis, a globally significant zoonosis caused by Brucella, poses a substantial impediment to the sustainable development of the livestock industry and threatens public health security. Traditional control and prevention strategies for brucellosis suffer from fundamental limitations. These include the time-consuming, biosafety risks associated with bacterial culture and the difficulty in differentiating between natural infection and vaccine-induced immunity with serological tests. There remains a challenge in accurately distinguishing vaccine strains from wild-type strains via genotyping methods. Furthermore, current practice relies on manual reporting and empirical judgment without real-time data support. Consequently, these challenges could hinder early detection and reduce the efficiency of large-scale screening, which leads to imprecise outbreak tracing and fails to meet the practical demands of modern livestock production for rapid diagnosis, intelligent prevention and control. In recent years, artificial intelligence (AI) and machine learning (ML) technologies have been widely applied in the field of veterinary microbiology, which could offer novel technical pathways for the prevention and control of brucellosis. This paper reviews the latest research progress in the application of AI for the rapid diagnosis of brucellosis, genomic analysis and antimicrobial resistance prediction of Brucella, epidemiological risk early warning and the development of drugs and vaccines. We further analyze the current limitations of AI in brucellosis research including the scarcity of high-quality, annotated datasets, insufficient model interpretability and low efficiency in on-site translation and implementation. The paper provides an outlook on the future development of AI-based intelligent prevention and control systems for brucellosis, which offer a theoretical reference and innovative perspectives for the fundamental research and clinical application of AI technologies. To the best of our knowledge, this is the first review to integrate AI applications across the entire chain of brucellosis prevention and control and to propose a conceptual framework for AI-driven intelligent brucellosis management.