Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2020 Jun 30;12(6):e8923.
doi: 10.7759/cureus.8923.

Artificial Intelligence: Is It Armageddon for Breast Radiologists?

Lawman Chiwome  1 , Onosetale M Okojie  2 , A K M Jamiur Rahman  3 , Faheem Javed  4 , Pousettef Hamid  5
Affiliations
Review

Artificial Intelligence: Is It Armageddon for Breast Radiologists?

Lawman Chiwome et al. Cureus. .

Abstract

Artificial Intelligence (AI) has taken radiology by storm, in particular, mammogram interpretation, and we have seen a recent surge in the number of publications on potential uses of AI in breast radiology. Breast cancer exerts a lot of burden on the National Health Service (NHS) and is the second most common cancer in the UK as of 2018. New cases of breast cancer have been on the rise in the past decade, while the survival rate has been improving. The NHS breast cancer screening program led to an improvement in survival rate. The expansion of the screening program led to more mammograms, thereby putting more work on the hands of radiologists, and the issue of double reading further worsens the workload. The introduction of computer-aided detection (CAD) systems to help radiologists was found not to have the expected outcome of improving the performance of readers. Unreliability of CAD systems has led to the explosion of studies and development of applications with the potential use in breast imaging. The purported success recorded with the use of machine learning in breast radiology has led to people postulating ideas that AI will replace breast radiologists. Of course, AI has many applications and potential uses in radiology, but will it replace radiologists? We reviewed many articles on the use of AI in breast radiology to give future radiologists and radiologists full information on this topic. This article focuses on explaining the basic principles and terminology of AI in radiology, potential uses, and limitations of AI in radiology. We have also analysed articles and answered the question of whether AI will replace radiologists.

Keywords: artificial intelligence; breast cancer; mammogram; radiology.

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Simple neural network compared to the deep neural network
Photo credit: Waldrop [17].
Figure 2
Figure 2. Schematic illustration of how a convolutional neural network extracts data and process it to give an output
Photo credit: Ribli et al. [18].
See this image and copyright information in PMC

References

    1. Breast cancer screening. [Feb;2020 ];https://www.nhs.uk/conditions/breast-cancer-screening/ 2018
    1. Breast cancer statistics. [Feb;2020 ];https://www.cancerresearchuk.org/health-professional/cancer-statistics/s... 2018
    1. Cancer registration statistics, England: first release, 2018. [Feb;2020 ];https://www.gov.uk/government/publications/cancer-registration-statistic... 2018 2017
    1. Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach. Dheeba J, Singh AN, Selvi TS. J Biomed Inform. 2014;49:45–52. - PubMed
    1. Quantifying the benefits and harms of screening mammography. Welch HG, Passow HJ. JAMA Intern Med. 2014;3:448–454. - PubMed

LinkOut - more resources