Artificial Intelligence in Cardiovascular Imaging: Current Landscape, Clinical Impact, and Future Directions

Abstract

Cardiovascular (CV) imaging is rapidly transforming with the advent of artificial intelligence (AI), automating and augmenting diagnostic pipelines in echocardiography, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear imaging. In this review, we summarize recent developments in convolutional neural networks for real-time echocardiographic interpretation, deep learning for coronary artery calcium scoring that achieves near-perfect agreement with manual methods, and AI-driven plaque quantification and stenosis detection on coronary CT angiography, which achieves an accuracy of ≥ 96%. FDA-approved platforms (e.g., Aidoc, HeartFlow, Caption Health) emphasize clinical translation, while automated segmentation and perfusion analysis in cardiac MRI produce Dice coefficients ≥ 0.93. We critically analyze persistent issues, algorithmic bias, explainability, data privacy, regulatory heterogeneity, and medico-legal liability. We also discuss risk-reduction tactics, such as federated learning and human-in-the-loop oversight. Reactive diagnostics will allow proactive, personalized treatment in the future, assuming we look ahead, thanks to multimodal AI, wearable sensors, and predictive analytics. For AI to fully optimize cardiovascular care, thorough validation, open algorithmic design, and interdisciplinary cooperation will be necessary.

Keywords: Artificial Intelligence; Cardiovascular Imaging; Deep Learning; Diagnostic Accuracy; Regulatory Challenges..

Figure 1. Timeline of FDA-approved AI tools in cardiovascular imaging (2013–2023).

Inspired by previously published sources and data in References , , , , and .