# Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?

## TL;DR

AI is transforming cardiovascular imaging by improving accuracy and efficiency, but challenges like data privacy and regulatory issues remain.

## Contribution

A comprehensive review of AI's current clinical applications and challenges in cardiovascular imaging.

## Key findings

- AI improves automated quantification and disease detection in cardiovascular imaging.
- AI can enhance clinical operations and patient experience when used with physician expertise.
- Challenges include lack of robust evidence, regulatory issues, and ethical concerns.

## Abstract

Cardiovascular imaging is essential in the diagnosis, phenotyping and prognostic assessment of cardiovascular disease. However, longstanding limitations constrain the accuracy, throughput, and scalability of cardiovascular imaging techniques. Artificial intelligence (AI) has demonstrated a diverse range of potential benefits across modalities, including echocardiography, computerised tomography, nuclear imaging, and magnetic resonance imaging. These benefits include automated quantification of key heart parameters, ability to improve traditional disease detection and phenotyping, and image reconstruction. While the use of AI in clinical workflows is still largely emerging, its significance is becoming established through numerous promising studies. The evidence reviewed indicates that AI can meaningfully enhance disease management, clinical operations and patient experience when used alongside physician expertise. However, several challenges restrict the widespread clinical implementation of AI, including a lack of robust prospective evidence, regulatory hurdles, bias in training datasets, and ethical drawbacks such as data privacy and accountability. Future developments should prioritise large-scale prospective and multicentre validation and address practical and ethical barriers to ensure AI can be utilised safely and effectively in clinical settings. This narrative review comprehensively analyses advances in AI-driven cardiovascular imaging with a focus on clinical implementation.