Cureus. 2026 Jul 17;18(7):e112841. doi: 10.7759/cureus.112841. eCollection 2026 Jul.
ABSTRACT
Cardiovascular diseases (CVDs) continue to be a major cause of death and morbidity throughout the world, and there is an increasing need for better diagnostic and predictive approaches. Existing methods do not fully reflect complex clinical interactions, and new computational methods possess greater capabilities. The limitations, including a lack of diversity in datasets, low generalizability, and limited interpretability, limit general use in the clinic. The review highlights the latest developments in artificial intelligence (AI) for diagnosing and predicting cardiovascular risk factors, with a focus on its clinical applications and current challenges. A literature review was conducted on machine learning (ML) and deep learning (DL) applications in imaging, electrocardiography (ECG), and predictive modeling. AI has been shown to improve diagnostic accuracy, facilitate earlier diagnosis, and enhance the ability to stratify disease risk compared with traditional methods. The seamless integration with wearable technologies ensures continuous monitoring and proactive management. While these developments help to ensure more accurate and individualized care, there are issues of validation, ethics, and integration. Moreover, integration of multi-modal data sources and real-time analytics enhances clinical decision-making and risk assessment. As technology continues to evolve, its scalability and applicability across various healthcare settings are expected to improve. In summary, AI has the potential to revolutionize cardiovascular care and enhance clinical outcomes by leveraging data-driven approaches.
PMID:42605286 | PMC:PMC13477592 | DOI:10.7759/cureus.112841