JMIR Publications

ABSTRACT

Background:

Artificial Intelligence (AI) has shown exponential growth and advancements, revolutionizing various fields, including healthcare. However, domain adaptation remains a significant challenge, as machine learning (ML) models often need to be applied across different healthcare settings with varying patient demographics and practices. This issue is critical for ensuring effective and equitable AI deployment. Cardiovascular diseases (CVD), the leading cause of global mortality with 17.9 million annual deaths, encompass conditions like coronary heart disease and hypertension. The increasing availability of medical data, coupled with AI advancements, offers new opportunities for early detection and intervention in cardiovascular events, leveraging AI's capacity to analyse complex datasets and uncover critical patterns.

Objective:

This review aims to examine AI methodologies combined with medical data to advance the intelligent monitoring and detection of cardiovascular diseases, identifying areas for further research to enhance patient outcomes and support early interventions.

Methods:

This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology to ensure a rigorous and transparent literature review process. This structured approach facilitated a comprehensive overview of the current state of research in this field.

Results:

Through the methodology utilized, there were retrieved 64 documents, 40 of which met the inclusion criteria. The reviewed articles demonstrate advancements in AI and ML for CVD detection, classification, prediction, diagnosis, and patient monitoring. Techniques such as ensemble learning, deep neural networks, and feature selection improve prediction accuracy over traditional methods. ML models predict cardiovascular events and risks, with applications in monitoring via wearable technology. The integration of AI in healthcare supports early detection, personalized treatment, and risk assessment, possibly improving the management of CVD.

Conclusions:

The study concludes that AI and ML techniques can improve the accuracy of cardiovascular disease classification, prediction, diagnosis, and monitoring. The integration of multiple data sources and non-invasive methods supports continuous monitoring and early detection. These advancements help enhance CVD management and patient outcomes, indicating the potential for AI to offer more precise and cost-effective solutions in healthcare.