Vol. 7, Issue 1, Part B (2025)

Artificial Intelligence (AI) and Machine Learning (ML) are transforming cardiovascular medicine by improving how we diagnose, predict, and personalize treatment for heart conditions. This paper explores the main ways AI is currently being used, such as refining Electrocardiogram (ECG) readings, enhancing echocardiography, and interpreting genetic and other “omics” data to tailor treatment plans and better predict patient risk.

Despite the promise, clinical adoption faces hurdles. Many AI models are complex and challenging to interpret, making it hard for clinicians to fully trust them, especially as deep learning models often act like “black boxes” where their inner workings are unclear. Data privacy concerns add another layer, emphasizing the need for responsible data use and transparency.

Looking forward, AI’s role in predicting heart disease and monitoring patient health in real time is promising, aided by big data, cloud computing, and the Internet of Things (IoT). New diagnostic tools, like Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs), improve accuracy, while “digital cardiac twins” personalized heart models offer a way to test treatments virtually to better predict individual outcomes.

AI is also playing a growing role in training future cardiologists, supporting early intervention strategies, and helping predict neurological outcomes for patients post-cardiac arrest. Tools predicting patient adherence to treatments may further enhance patient management, especially in heart failure. However, realizing AI’s full potential in heart care depends on overcoming data transparency, algorithmic bias, and ethical challenges. Collaboration between technology experts, healthcare providers, and regulators is critical to making AI a trusted partner in preventive and personalized heart care.