a brief review on utilizing artificial intelligence in the diagnosis of cardiac diseases with diagnostic imaging focusing on echocardiography

Artificial intelligence refers to systems that allow computers or machines to perform or mimic human thinking. machine learning is a further evolution of ai and allows the computer to continually improve from its experiences, without explicit instruction from a human. although deep learning has been explored extensively for computer-aided medical imaging diagnosis in human medicine, very little has been done in veterinary medicine.echocardiography, this usually takes the form of reinforcement learning rather than completely unsupervised learning, with the computer making inferences based on the sonographer’s acceptance or rejection of the computer’s analysis. an ai network can be trained to adequately measure linear lv dimensions, with performance indistinguishable from that of experts.some studies in this field shows that the high accuracy of the algorithm in predicting the myxomatous mitral valve disease stage ,suggest that ai could stand as a useful support tool in the interpretation of canine thoracic radiographs. it has been indicated that the ahvi outperformed the current clinical standard in predicting cardiac enlargement, a common but often fatal health condition for small old dogs.artificial intelligence (ai) is capable of executing laborious and time-intensive tasks, such as tracing endocardial borders. additionally, it enhances efficiency and consistently produces optimal work output without experiencing fatigue or energy depletion. ai can assist novice practitioners in image interpretation and acquisition, while also reducing variability and enhancing reproducibility. it has been recommended that researchers and practitioners concentrate on this area to address the gap in functional artificial intelligence within veterinary practice and to maximize its potential benefits.