INNOVATIONS IN ANIMAL HEALTHCARE: A MACHINE LEARNING PERSPECTIVE ON VETERINARY DIAGNOSTICS

Abstract

The application of machine learning (ML) in veterinary diagnostics represents a paradigm shift in the field, promising to revolutionize the accuracy and efficiency of disease detection and monitoring. This paper delves into the diverse applications of ML techniques in veterinary medicine, ranging from the analysis of medical imaging data to the integration of genomic and clinical information. The proposed system emphasizes the development of sophisticated algorithms to handle multifaceted data, fostering a comprehensive understanding of an animal's health. Through empirical validation, the study demonstrates the significant strides made in diagnostic accuracy, showcasing the versatility of ML models across different species. This exploration highlights the transformative potential of ML applications in veterinary diagnostics, paving the way for improved healthcare outcomes and informed decision-making.