Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist

K. R. Siegersma, T. Leiner, D. P. Chew, Y. Appelman, L. Hofstra, J. W. Verjans

Research output: Contribution to journalReview article

6 Citations (Scopus)
7 Downloads (Pure)

Abstract

Healthcare, conceivably more than any other area of human endeavour, has the greatest potential to be affected by artificial intelligence (AI). This potential has been shown by several reports that demonstrate equal or superhuman performance in medical tasks that aim to improve efficiency, diagnosis and prognosis. This review focuses on the state of the art of AI applications in cardiovascular imaging. It provides an overview of the current applications and studies performed, including the potential value, implications, limitations and future directions of AI in cardiovascular imaging.

It is envisioned that AI will dramatically change the way doctors practise medicine. In the short term, it will assist physicians with easy tasks, such as automating measurements, making predictions based on big data, and putting clinical findings into an evidence-based context. In the long term, AI will not only assist doctors, it has the potential to significantly improve access to health and well-being data for patients and their caretakers. This empowers patients. From a physician’s perspective, reliable AI assistance will be available to support clinical decision-making. Although cardiovascular studies implementing AI are increasing in number, the applications have only just started to penetrate contemporary clinical care.

Original languageEnglish
Pages (from-to)403-413
Number of pages11
JournalNetherlands Heart Journal
Volume27
Issue number9
DOIs
Publication statusPublished - 1 Sep 2019
Externally publishedYes

Bibliographical note

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Keywords

  • Artificial intelligence
  • Cardiac imaging techniques
  • Clinical decision-making
  • Machine learning
  • Medical imaging

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