AI Integration in Veterinary Practice: Improving Care with Technology and Data

Abstract

Artificial Intelligence is altering how veterinarians work by making it easier to make quick, accurate, and evidence-based treatment decisions. Its integration improves diagnosis of diseases, radiography, advanced analytics, and medical management for many different species. This study looks at the newest developments in AI applications in veterinary medicine, concentrating on four areas: electronic health records (EHR), radiology, natural language processing (NLP), and advanced analytics. Deep learning models, especially CNNs, have done quite well in medicine. The accuracy of imaging and the strong prediction power of ML algorithms used on EHRs are both impressive. NLP frameworks are used to make sense of unstructured medical records, and predictive models are helping clinicians identify diseases earlier. Even with these advancements, it is still challenging to put them into action because of challenges including malfunctioning data systems, a lack of structure, regulatory issues, and a lack of resources. This paper looks at research that were published between 2022 and 2025 and talks about methods, data models, and performance measures. The research states that AI shouldn't take the place of what veterinarians know, but instead work with it. AI needs to do a better job, manage data better, and work better with other departments if people are going to trust it with animal health, ethics, and medicine.

Ähnliche Arbeiten