Innovations, Applications, and Future Trends in Veterinary Diagnostic Technologies

Abstract

Veterinary diagnostics is undergoing a significant transformation driven by technological advancements, extending its scope from the traditional confirmation of specific pathogens to the continuous, dynamic surveillance of animal population’s health. This paradigm shift has the potential to enable more timely disease control, precise intervention, and enhanced public health security. Traditional clinical and laboratory diagnostic methods, such as microbial culture, serological assays, and nucleic acid‐based polymerase chain reaction, form the cornerstone of the current diagnostic framework and are widely applied based on varying detection needs and practical environments. Nonetheless, the field is experiencing profound innovation. Firstly, novel detection technologies are emerging, such as digital PCR (dPCR), CRISPR‐Cas‐based molecular diagnostic tools, next‐generation sequencing (NGS), and metagenomic sequencing. These technologies have not only achieved breakthroughs in sensitivity and specificity but, more importantly, enable the unbiased discovery of novel pathogens. Secondly, the deep integration of artificial intelligence (AI) and big data is reshaping the diagnostic pipeline. By consolidating and analyzing multimodal information streams from imaging, genomics, wearable devices, and production data, AI algorithms can provide objective, quantitative decision support, facilitating a transition from post‐symptomatic diagnosis towards predictive and preventive health management. This scoping review systematically summarizes both mainstream and emerging veterinary diagnostic technologies, elaborates and discusses their advantages and limitations as well as future developmental directions, while highlighting that the combined application of multiple methods represents an optimal diagnostic strategy.