Abstract
Background: In particular, cardiovascular disease still continues to be the major cause of death globally and costs approximately $17. 9 million deaths annually. The pathophysiology of cardiovascular disease and coronary artery disease, heart failure, and arrhythmias makes early diagnosis vital and important. However, in ECG and echocardiogram a human element is involved and these tests may not be accurate because of variability and also may not have the capacity to pick up minor disease patterns. More prominently, the advancement of effective CVD diagnostic tools and better approaches in management is long overdue because CVD is a leading cause of death and places a burden on the health systems. Artificial intelligence (AI) as advanced technology has been integrated into the contemporary healthcare system, and has the ability to analyse data, identify patterns, and address unique needs of the patient and especially in cardiology.
Aim: To this end, the present research’s purpose is to assess the role of artificial intelligence in the diagnosis, treatment, and management of cardiovascular diseases. In particular, the investigation aims to assess the current application of AI for enhancing diagnostic performance, individualised treatment, prognosis, and cost-effective healthcare services.
Method: This study employed both a literature review on AI in cardiology and experimentation of artificial intelligence algorithms. It was collected from the electronic health record, medical imaging data and patient monitoring devices. A number of AI modelling such as machine learning and deep learning was used to interpret ECG and echocardiogram data, evaluate treatment strategies, and CVD prognosis. Validity of performance was tested by the use of sensitivity, specificity and accuracy. When designing the study, issues of place and protection of data privacy, as well as obtaining informed consent from patients played an important role due to compliance with healthcare standards.
Results: The study found out that AI based diagnostics offered more precision and speed than conventional diagnostic tests. AI achieved a 98. 7% and 96% accuracy respectively in ECG interpretation by Computer algorithm which is similar to the 97% accuracy of a Cardiologist. In the analysis of echocardiogram results, the new model overcame its shortcoming by achieving 1% while the traditional model only had an 85. 3% and 87. 2% accuracy, respectively. I believe AI was also used in determining treatment plans since patients’ data was analysed thereby reducing adverse effects and enhancing treatment. Further, AI trended a very successful prognosis of cardiovascular events with an achievement of 92 percent. 4%. The economic evaluation indicated that AI eliminated unnecessary tests and hospital readmissions and therefore provided a 70 percent saving on CVD management cost.
Conclusion: This paper shows that a new approach to the development of cardiovascular medicine is possible with the help of AI, increasing diagnostic, treatment, and prognostic accuracy with respect to the human approach. The incorporation of AI in cardiovascular practice serves to enhance general cardiovascular care, increases patient’s quality of life and even help to reduce health expenditures while eradicating the burden created by CVDs in the entire globe. In the future years, the usage of AI technology is believed to grow especially in preventive care and long-term disease management with possibilities and opportunities for change in the overall healthcare system.
Keywords: Cardiovascular Disease, Artificial Intelligence, Diagnosis, Treatment Personalization, Predictive Analytics, Healthcare Cost, Machine Learning, Deep Learning.