The Artificial Intelligence and Genomics in Veterinary Microbiology: Applications in Disease Diagnosis, Surveillance and Antimicrobial Resistance Monitoring

Abstract

Veterinary microbiology has undergone substantial transformation in recent years through the integration of artificial intelligence (AI) and genomic technologies. The emergence of next-generation sequencing (NGS), whole-genome sequencing (WGS), metagenomics, and advanced bioinformatics has generated unprecedented volumes of biological data, necessitating sophisticated computational approaches for data interpretation. Artificial intelligence, encompassing machine learning, deep learning, and predictive analytics, has emerged as a transformative tool capable of extracting meaningful insights from complex datasets and improving decision-making processes in animal health management. The convergence of AI and genomics has significantly enhanced pathogen detection, disease diagnosis, outbreak surveillance, antimicrobial resistance (AMR) monitoring, and epidemiological investigations. AI-assisted diagnostic systems can rapidly analyze microscopic images, molecular assay outputs, and genomic sequences, enabling accurate and timely disease detection. Similarly, genomic surveillance combined with machine learning facilitates early identification of emerging pathogens, prediction of outbreak dynamics, and monitoring of pathogen evolution. In the context of antimicrobial resistance, AI-driven genomic analyses provide powerful tools for detecting resistance determinants, forecasting resistance trends, and supporting antimicrobial stewardship programs. Despite substantial progress, challenges including data quality, algorithm transparency, infrastructure limitations, and regulatory concerns continue to hinder widespread adoption. This review summarizes recent advances in the application of artificial intelligence and genomics in veterinary microbiology, focusing on disease diagnosis, surveillance, and antimicrobial resistance monitoring. Furthermore, current challenges and future prospects are discussed within the framework of precision veterinary medicine and the One Health approach.