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Review
. 2023 Jun 28:5:1201392.
doi: 10.3389/fdgth.2023.1201392. eCollection 2023.

Successes and challenges of artificial intelligence in cardiology

Bert Vandenberk  1   2 , Derek S Chew  1 , Dinesh Prasana  3   4 , Sunny Gupta  3 , Derek V Exner  1
Affiliations
Review

Successes and challenges of artificial intelligence in cardiology

Bert Vandenberk et al. Front Digit Health. .

Abstract

In the past decades there has been a substantial evolution in data management and data processing techniques. New data architectures made analysis of big data feasible, healthcare is orienting towards personalized medicine with digital health initiatives, and artificial intelligence (AI) is becoming of increasing importance. Despite being a trendy research topic, only very few applications reach the stage where they are implemented in clinical practice. This review provides an overview of current methodologies and identifies clinical and organizational challenges for AI in healthcare.

Keywords: artificial intelligence; big data; cardiology; electrophysiolgy; synthetic data.

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Conflict of interest statement

DP and SG are employed by Intelense Inc. DP is employed by IOT/AI - Caliber Interconnect Pvt Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Number of publications on “artificial intelligence in healthcare” according to PubMed.
Figure 2
Figure 2
Overview of development and implementation of AI applications.
Figure 3
Figure 3
The cardinal features of big data. Variability refers to the consistency of the data over time, while variety reflects the wide range of types of data, such as images or videos. Volume refers to the magnitude of data which is generated in short time periods (velocity). The generated data yield high value, both scientific and economic, but depends on the reliability and accurateness (veracity). Adapted from Pablo et al. (2).
Figure 4
Figure 4
The relation between artificial intelligence, machine learning, deep learning, and natural language processing.
Figure 5
Figure 5
Schematic overview of a typical workflow in machine learning.
See this image and copyright information in PMC

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