Review
. 2020 Aug; 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
  • PMID: 32760624
  •     34 References

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.

Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring.
Michiel Kallenberg, Kersten Petersen, +8 authors, Martin Lillholm.
IEEE Trans Med Imaging, 2016 Feb 26; 35(5). PMID: 26915120
Highly Cited.
The impact of artificial intelligence in medicine on the future role of the physician.
Abhimanyu S Ahuja.
PeerJ, 2019 Oct 09; 7. PMID: 31592346    Free PMC article.
Improved Cancer Detection Using Artificial Intelligence: a Retrospective Evaluation of Missed Cancers on Mammography.
Alyssa T Watanabe, Vivian Lim, +5 authors, Christopher E Comstock.
J Digit Imaging, 2019 Apr 24; 32(4). PMID: 31011956    Free PMC article.
Artificial intelligence in digital breast pathology: Techniques and applications.
Asmaa Ibrahim, Paul Gamble, +4 authors, Emad A Rakha.
Breast, 2020 Jan 15; 49. PMID: 31935669    Free PMC article.
Review.
Collaboration, campaigns and champions for appropriate imaging: feedback from the Zagreb workshop.
D Remedios, B Brkljacic, +3 authors, J Vassileva.
Insights Imaging, 2018 Mar 14; 9(2). PMID: 29532320    Free PMC article.
Artificial intelligence in radiology.
Ahmed Hosny, Chintan Parmar, +2 authors, Hugo J W L Aerts.
Nat Rev Cancer, 2018 May 20; 18(8). PMID: 29777175    Free PMC article.
Highly Cited. Review.
Deep Learning in Medical Imaging: General Overview.
June-Goo Lee, Sanghoon Jun, +4 authors, Namkug Kim.
Korean J Radiol, 2017 Jul 04; 18(4). PMID: 28670152    Free PMC article.
Highly Cited. Review.
Trends in radiology and experimental research.
Francesco Sardanelli.
Eur Radiol Exp, 2017 Jan 01; 1(1). PMID: 29708170    Free PMC article.
Implementing Machine Learning in Radiology Practice and Research.
Marc Kohli, Luciano M Prevedello, Ross W Filice, J Raymond Geis.
AJR Am J Roentgenol, 2017 Jan 27; 208(4). PMID: 28125274
Highly Cited. Review.
Adapting to Artificial Intelligence: Radiologists and Pathologists as Information Specialists.
Saurabh Jha, Eric J Topol.
JAMA, 2016 Nov 30; 316(22). PMID: 27898975
Highly Cited.
News Feature: What are the limits of deep learning?
M Mitchell Waldrop.
Proc Natl Acad Sci U S A, 2019 Jan 24; 116(4). PMID: 30670601    Free PMC article.
Deep Learning for Health Informatics.
Daniele Ravi, Charence Wong, +4 authors, Guang-Zhong Yang.
IEEE J Biomed Health Inform, 2017 Jan 06; 21(1). PMID: 28055930
Highly Cited. Review.
Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans.
Jie-Zhi Cheng, Dong Ni, +6 authors, Chung-Ming Chen.
Sci Rep, 2016 Apr 16; 6. PMID: 27079888    Free PMC article.
Highly Cited.
Artificial neural networks: opening the black box.
J E Dayhoff, J M DeLeo.
Cancer, 2001 Apr 20; 91(8 Suppl). PMID: 11309760
Review.
Addition of tomosynthesis to conventional digital mammography: effect on image interpretation time of screening examinations.
Pragya A Dang, Phoebe E Freer, +2 authors, Elizabeth A Rafferty.
Radiology, 2013 Dec 21; 270(1). PMID: 24354377
Radiomics: Images Are More than Pictures, They Are Data.
Robert J Gillies, Paul E Kinahan, Hedvig Hricak.
Radiology, 2015 Nov 19; 278(2). PMID: 26579733    Free PMC article.
Highly Cited.
Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning.
Jinhua Wang, Xi Yang, +3 authors, Li Li.
Sci Rep, 2016 Jun 09; 6. PMID: 27273294    Free PMC article.
Current Applications and Future Impact of Machine Learning in Radiology.
Garry Choy, Omid Khalilzadeh, +7 authors, Keith J Dreyer.
Radiology, 2018 Jun 27; 288(2). PMID: 29944078    Free PMC article.
Highly Cited. Review.
Quantifying the benefits and harms of screening mammography.
H Gilbert Welch, Honor J Passow.
JAMA Intern Med, 2014 Jan 01; 174(3). PMID: 24380095
Highly Cited.
THE CODING OF ROENTGEN IMAGES FOR COMPUTER ANALYSIS AS APPLIED TO LUNG CANCER.
G S LODWICK, T E KEATS, J P DORST.
Radiology, 1963 Aug 01; 81. PMID: 14053755
Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection.
Constance D Lehman, Robert D Wellman, +4 authors, Breast Cancer Surveillance Consortium.
JAMA Intern Med, 2015 Sep 29; 175(11). PMID: 26414882    Free PMC article.
Highly Cited.
The future of radiology augmented with Artificial Intelligence: A strategy for success.
Charlene Liew.
Eur J Radiol, 2018 Apr 25; 102. PMID: 29685530
Review.
Artificial intelligence (AI) systems for interpreting complex medical datasets.
R B Altman.
Clin Pharmacol Ther, 2017 Feb 10; 101(5). PMID: 28182259
Review.
Artificial intelligence. Fears of an AI pioneer.
Stuart Russell, John Bohannon.
Science, 2015 Jul 18; 349(6245). PMID: 26185241
Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States.
Filippo Pesapane, Caterina Volonté, Marina Codari, Francesco Sardanelli.
Insights Imaging, 2018 Aug 17; 9(5). PMID: 30112675    Free PMC article.
Review.
Detecting and classifying lesions in mammograms with Deep Learning.
Dezső Ribli, Anna Horváth, +2 authors, István Csabai.
Sci Rep, 2018 Mar 17; 8(1). PMID: 29545529    Free PMC article.
Highly Cited.
Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach.
J Dheeba, N Albert Singh, S Tamil Selvi.
J Biomed Inform, 2014 Feb 11; 49. PMID: 24509074
Content analysis of reporting templates and free-text radiology reports.
Yi Hong, Charles E Kahn.
J Digit Imaging, 2013 Apr 05; 26(5). PMID: 23553231    Free PMC article.
Artificial Intelligence: Threat or Boon to Radiologists?
Michael Recht, R Nick Bryan.
J Am Coll Radiol, 2017 Aug 23; 14(11). PMID: 28826960
Breast cancer detection using deep convolutional neural networks and support vector machines.
Dina A Ragab, Maha Sharkas, Stephen Marshall, Jinchang Ren.
PeerJ, 2019 Feb 05; 7. PMID: 30713814    Free PMC article.
Stand-alone artificial intelligence - The future of breast cancer screening?
Ioannis Sechopoulos, Ritse M Mann.
Breast, 2020 Jan 14; 49. PMID: 31927164    Free PMC article.
Deep Learning: A Primer for Radiologists.
Gabriel Chartrand, Phillip M Cheng, +5 authors, An Tang.
Radiographics, 2017 Nov 14; 37(7). PMID: 29131760
Highly Cited. Review.
Imaging study protocol selection in the electronic medical record.
Peter B Sachs, Geralyn Gassert, +3 authors, Danielle Decoteau.
J Am Coll Radiol, 2013 Apr 11; 10(3). PMID: 23571063
Radiomics: the bridge between medical imaging and personalized medicine.
Philippe Lambin, Ralph T H Leijenaar, +17 authors, Sean Walsh.
Nat Rev Clin Oncol, 2017 Oct 05; 14(12). PMID: 28975929
Highly Cited. Review.