The Use of Artificial Intelligence in Predicting Cardiovascular Events in High-Risk Patients

Abstract

The article aims to explore the potential of artificial intelligence in predicting cardiovascular events in high-risk patients, by reviewing studies recent studies and the discussion of the advantages and limitations of artificial intelligence techniques in this area. The systematic review included 12 studies that addressed the application of artificial intelligence algorithms to predict cardiovascular events in high-risk patients, such as those with a prior history of cardiovascular disease, hypertension, or diabetes. The studies used a variety of data sources, including medical imaging, clinical data, and genomic information. The results showed that artificial intelligence algorithms were significantly more accurate than traditional cardiovascular risk prediction models, especially in identifying patients at high risk of cardiovascular events. However, the studies also highlighted some limitations in the application of artificial intelligence, such as the need for large training datasets and the lack of transparency in the interpretation of results. The studies also evaluated the performance of different artificial intelligence algorithms, including artificial neural networks, decision trees and reinforcement learning algorithms. Although the results were variable, in general, neural network algorithms showed the best accuracy in predicting cardiovascular events. Patients' characteristics were also evaluated in the studies, and it was observed that clinical variables such as age, sex, blood pressure and cholesterol were the main predictors of cardiovascular events. The inclusion of genomic data also showed potential to improve prediction accuracy. Finally, the review discussed the advantages and limitations of artificial intelligence in predicting cardiovascular events in high-risk patients. Although artificial intelligence has significant potential to improve prediction accuracy, its implementation in clinical practice must take into account the limitations of algorithms and the need for transparency in the interpretation of results. In summary, the review highlights the potential of artificial intelligence in predicting cardiovascular events in high-risk patients, but also underscores the importance of a careful approach in its implementation in clinical practice.

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