Artificial Intelligence-Based Methods for Precision Cardiovascular Medicine

• Published in: Journal of personalized medicine 2023-08, Vol.13 (8), p.1268
• Main Authors: Mohsen, Farida, Al-Saadi, Balqees, Abdi, Nima, Khan, Sulaiman, Shah, Zubair
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Cardiology; Cardiovascular agents; Cardiovascular disease; Cardiovascular diseases; Computational linguistics; Datasets; Deep learning; Development and progression; Diagnosis; Drug therapy; Electronic health records; Electronic medical records; Language processing; Learning algorithms; Machine learning; Medical imaging; Medical prognosis; Medical research; Medicine, Experimental; Mortality; Natural language interfaces; Neural networks; Pediatrics; Phenotyping; Precision medicine; Predictions; Prognosis; Research methodology; Review
• ISSN: 2075-4426; 2075-4426
• DOI: 10.3390/jpm13081268

Precision medicine has the potential to revolutionize the way cardiovascular diseases are diagnosed, predicted, and treated by tailoring treatment strategies to the individual characteristics of each patient. Artificial intelligence (AI) has recently emerged as a promising tool for improving the accuracy and efficiency of precision cardiovascular medicine. In this scoping review, we aimed to identify and summarize the current state of the literature on the use of AI in precision cardiovascular medicine. A comprehensive search of electronic databases, including Scopes, Google Scholar, and PubMed, was conducted to identify relevant studies. After applying inclusion and exclusion criteria, a total of 28 studies were included in the review. We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. As a result, most of these studies focused on prediction (50%), followed by diagnosis (21%), phenotyping (14%), and risk stratification (14%). A variety of machine learning models were utilized in these studies, with logistic regression being the most used (36%), followed by random forest (32%), support vector machine (25%), and deep learning models such as neural networks (18%). Other models, such as hierarchical clustering (11%), Cox regression (11%), and natural language processing (4%), were also utilized. The data sources used in these studies included electronic health records (79%), imaging data (43%), and omics data (4%). We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. The results of the review showed that AI has the potential to improve the performance of cardiovascular disease diagnosis and prognosis, as well as to identify individuals at high risk of developing cardiovascular diseases. However, further research is needed to fully evaluate the clinical utility and effectiveness of AI-based approaches in precision cardiovascular medicine. Overall, our review provided a comprehensive overview of the current state of knowledge in the field of AI-based methods for precision cardiovascular medicine and offered new insights for researchers interested in this research area.