AI-Driven Early Detection of Cardiovascular Diseases: Reducing Healthcare Costs and improving patient Outcomes

Abstract

Purpose: The main goal from this study is to discuss the main features of Artificial Intelligence (AI) as well as their applicability for early Cardiovascular Diseases (CVDs) detection. It is not only about examining the possibility of applying AI diagnostic tools to increase the efficiency of early diagnosis but also to minimize overall healthcare expenses while increasing patient satisfaction. Some of the emerging research problems that the study seeks to resolve are about the delay in diagnosing the disease and the expenses that accompany the disease and individually tailored treatment plans that patients require. Materials and Methods: The systematic review approach is applied to assess 15 research articles published between 2012 and 2024, and that were derived from standard databases and specified AI usage for the cardiovascular diagnostic field, cost, and patients. The criteria for inclusion were due to the availability of empirical studies and features of methodological quality. To learn more about challenges & opportunities of AI in cardiovascular healthcare, we also applied thematic analysis to the data collected. Results: It was seen that by integrating AI algorithms the diagnosis of CVDs became more accurate and less time consuming. Machine learning models were able to yield both significant predictive accuracy for patient risk stratification with the ability for early treatment and intervention. The findings showed that the use of AI in clinical practice led to reduction of cost since patients required fewer invasive procedures and admissions. Additionally, exceptional patient care outcomes and satisfaction, due to the development of individual therapy profiles based on artificial intelligence studies, were achieved. Besides, the review also discussed the ethical issue and the matters of data protection relevant to AI applications. Conclusion: Now the concept of using AI technologies in cardiovascular healthcare holds the potential to transform disease management. This integration of contemporized diagnostic equipment coupled to conventional medicine is crucial to cater for patient’s needs, improve health lives and overall efficiency and affordability of healthcare services. This has been the direction of this study to show that more research and development are needed in AI to allow for proper changing of cardiovascular disease diagnosis and management.