# Deep Learning in Cardiology

## TL;DR

This paper reviews how deep learning techniques are applied to cardiology, highlighting their advantages, limitations, and potential directions for clinical implementation in diagnosis, prediction, and intervention.

## Contribution

It provides a comprehensive survey of deep learning applications in cardiology, emphasizing current challenges and future prospects for clinical adoption.

## Key findings

- Deep learning improves accuracy in cardiology diagnostics.
- Limitations include data quality and interpretability issues.
- Future directions focus on clinical integration and real-world validation.

## Abstract

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.