evolution of veterinary medicine diagnosis by deep learning and artificial intelligence

The integration of artificial intelligence (ai) and deep learning in veterinary medicine is revolutionizing diagnostic practices, enhancing accuracy, and improving patient care. this paper reviews 37 research articles from databases such as elsevier, springer, pubmed, and web of science, focusing on the applications, benefits, and challenges associated with ai in veterinary diagnostics. key areas of impact include automated diagnosis, medical image analysis, predictive analytics, and personalized treatment plans. additionally, the use of deep learning for microscopic diagnosis in parasitology is explored, highlighting its effectiveness in identifying parasitic infections through advanced image analysis techniques. while ai presents significant opportunities for improved veterinary care, challenges such as data privacy, algorithmic bias, and ethical considerations must be addressed. this study underscores the potential of ai and deep learning to transform veterinary diagnostics, providing the way for enhanced animal healthcare.