Artificial Intelligence in Veterinary Diagnostics and Healthcare

Artificial intelligence is beginning to influence veterinary diagnostics by assisting with image interpretation, pattern recognition and prediction from large clinical datasets. Algorithms can be trained to evaluate radiographs, ultrasound images, dermatological photographs, pathology slides or retinal images, and to combine laboratory values and patient history into risk estimates. In production animals, AI may also use sensor data to flag animals with abnormal activity, rumination, temperature or milk characteristics. These tools can support faster screening and help prioritise cases, particularly when specialist expertise is limited. However, AI does not remove the need for a veterinary examination. Diagnostic performance depends on the quality and representativeness of training data, and a model may perform differently across species, breeds, equipment or clinical settings. False negative and false positive predictions can both have important consequences. Veterinary AI therefore requires validation, clear reporting of uncertainty and professional oversight. Patient records and images must also be handled with appropriate privacy and cybersecurity safeguards. The most useful role for AI is as decision support: it can draw attention to subtle patterns, reduce repetitive work and improve consistency, while the veterinarian integrates the output with history, physical findings, laboratory results and the owner's circumstances.