AI-Driven Real-Time Heart Health Monitoring: A Comprehensive Review of Artificial Intelligence, IoMT, and Real-Time Analytics in Cardiac Care

Authors:

Sadiya A, Syed Irfan A, Arram B, Daniya Y, Ashfan B, Pranay P

Abstract— cardiovascular diseases (CVDs) cause roughly 18 million deaths annually, which results in cardiovascular diseases being the most prominent cause of death in the world. Because of this, effective control of digital cardiovascular diseases due to the need for constant, ongoing assessments and evaluations. ECGs and Holter monitors are the most use-d devices in digital cardiovascular monitoring, and these devices ineffective in chronic monitoring due to the short, sporadic evaluations of heart activities. Most recently, the triad of AI, IoMT, and wearables has provided the digital monitoring of cardiovascular conditions at an unprecedented quality and cost. The main goal of this paper is to analyze the systems developed for AI-enabled continuous monitoring of the heart.

In this study, over 70 peer-reviewed articles dated 2019 to 2025 were reviewed and analyzed. In this case, the goal of this study is to develop a taxonomy that would create a stronger correlation between the AI methodologies, the degree of implementation, the cardiac functionalities, the data utilized, and the data utilized. A critique of ML and DL, as well as Edge/Federated AI, is presented, along with the systems' issues of ethics, privacy, and regulation. Finally, a roadmap of fully integrated intelligent systems in cardiovascular medicine, which are meant to be intuitive, energy-efficient, and inter-operable, is presented.

Keywords— Artificial Intelligence, ECG Monitoring, Deep Learning, Internet of Medical Things, Edge AI, Real-Time Analytics, Federated Learning, Telemedicine, Cardiovascular Diseases