An Overview of Artificial Intelligence Applications in Prediction and Diagnosis of Diseases Occurrence in Veterinary Medicine: Challenges and Techniques

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Article Type:
Review Article (دارای رتبه معتبر)
Abstract:

Early diagnosis of diseases is one of the main goals of health and wellness centers. Timely diagnosis can reduce the potential damage of diseases. The importance of this issue in veterinary medicine multiplies due to its combination with economic goals. Therefore, a predictive approach is necessary for early diagnosis of diseases. This approach should be evidence-based and highly accurate. It should also be economically efficient. Artificial intelligence is the simulation of human intelligence and judgment by a computer or a robot that is programmed or trained to perform tasks that normally need human abilities. The emergence of artificial intelligence and machine learning techniques in today's world has improved the existing functions in health care systems. So that with the application of this technology, a significant progress has been made in the procedures of event prediction and disease diagnosis, management and health at the macro level, etc. Furthermore, the scope of diagnosable diseases is extensive, encompassing any ailment for which relevant data can be processed by artificial intelligence algorithms. The trained model has the capability to diagnose a wide range of diseases, with accuracy contingent upon factors such as disease indicators, collected data, and other pertinent variables. In this review article, the most important applications of artificial intelligence in veterinary medicine will be mentioned, and in general, these applications will be examined in various fields such as diagnosis of common diseases, differential diagnosis, prediction of disease occurrence, veterinary diagnostic imaging techniques, veterinary clinical pathology, etc. In addition, the challenges in this field will also be mentioned. This article is a review of recent studies in this fiel.

Language:
Persian
Published:
Journal of Eltiam, Volume:10 Issue: 2, 2024
Pages:
71 to 87
https://www.magiran.com/p2721869  
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