Artificial Intelligence in Radiology

Maged Naser, Mohamed MN, Lamia H. Shehata


The quick improvement of artificial intelligence (AI) has led to its boundless use in numerous industries, including medical care. Artificial intelligence can possibly be an extraordinary innovation that will fundamentally affect tolerant consideration.

Especially, AI has a promising part in radiology, wherein PCs are essential and new technological progresses are regularly searched out and adopted early in clinical practice. We present an outline of the essential meanings of normal terms, the advancement of an AI ecosystem in imaging and its incentive in relieving the difficulties of usage in clinical practice.


Radiology, Artificial Intelligence, Machine Learning

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Bhargavan, Mythreyi, and Jonathan H. Sunshine. "Utilization of radiology services in the United States: levels and trends in modalities, regions, and populations." Radiology 234.3 (2005): 824-832.

Smith-Bindman, Rebecca, Diana L. Miglioretti, and Eric B. Larson. "Rising use of diagnostic medical imaging in a large integrated health system." Health affairs 27.6 (2008): 1491-1502.

Lee, Christophe I., Curtis P. Langlotz, and Joann G. Elmore. "Implications of direct patient online access to radiology reports through patient web portals." Journal of the American College of Radiology 13.12 (2016): 1608-1614.

Minsky, Marvin. "Steps toward artificial intelligence." Proceedings of the IRE 49.1 (1961): 8-30.

Sogani, Julie, et al. "Artificial intelligence in radiology: the ecosystem essential to improving patient care." (2020): A3-A6.

Allen, Bibb, Robert Gish, and Keith Dreyer. "The Role of an Artificial Intelligence Ecosystem in Radiology." Artificial Intelligence in Medical Imaging. Springer, Cham, 2019. 291-327.

Mazurowski, Maciej A., et al. "Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI." Journal of magnetic resonance imaging 49.4 (2019): 939-954.

Gillies, Robert J., Paul E. Kinahan, and Hedvig Hricak. "Radiomics: images are more than pictures, they are data." Radiology 278.2 (2016): 563-577.

Langlotz, Curtis P., et al. "A roadmap for foundational research on artificial intelligence in medical imaging: from the 2018 NIH/RSNA/ACR/The Academy Workshop." Radiology 291.3 (2019): 781-791.

McGinty, Geraldine B., and Bibb Allen. "The ACR data science institute and AI advisory group: harnessing the power of artificial intelligence to improve patient care." Journal of the American College of Radiology 15.3 (2018): 577-579.

Allen Jr, Bibb, et al. "A road map for translational research on artificial intelligence in medical imaging: from the 2018 National Institutes of Health/RSNA/ACR/The Academy Workshop." Journal of the American College of Radiology 16.9 (2019): 1179-1189.

Solenov, Dmitry, Jay Brieler, and Jeffrey F. Scherrer. "The potential of quantum computing and machine learning to advance clinical research and change the practice of medicine." Missouri medicine 115.5 (2018): 463.

Martin, Anne B., et al. "National health care spending in 2017: growth slows to post–Great Recession rates; share of GDP stabilizes." Health Affairs (2019): 10-1377.

Sogani, Julie, et al. "Artificial intelligence in radiology: the ecosystem essential to improving patient care." (2020): A3-A6.

Kolachalama, Vijaya B., and Priya S. Garg. "Machine learning and medical education." NPJ digital medicine 1.1 (2018): 1-3.

Ravì, Daniele, et al. "Deep learning for health informatics." IEEE journal of biomedical and health informatics 21.1 (2016): 4-21.

Pesapane, Filippo, Marina Codari, and Francesco Sardanelli. "Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine." European radiology experimental 2.1 (2018): 35.

Allen, Bibb, and Keith Dreyer. "The artificial intelligence ecosystem for the radiological sciences: ideas to clinical practice." Journal of the American College of Radiology 15.10 (2018): 1455-1457.

Fazal, Mohammad Ihsan, et al. "The past, present and future role of artificial intelligence in imaging." European journal of radiology 105 (2018): 246-250.

Paterson, Mary A., et al. "Achieving the triple aim: A curriculum framework for health professions education." American journal of preventive medicine 49.2 (2015): 294-296.

Bodenheimer, Thomas, and Christine Sinsky. "From triple to quadruple aim: care of the patient requires care of the provider." The Annals of Family Medicine 12.6 (2014): 573-576.

Ellenbogen, Paul H. "Imaging 3.0: what is it?." Journal of the American college of Radiology 10.4 (2013): 229.

Aminololama-Shakeri, Shadi, and Javier E. López. "The doctor-patient relationship with artificial intelligence." American Journal of Roentgenology 212.2 (2019): 308-310.

Langlotz, Curtis P. "Will artificial intelligence replace radiologists?." (2019): e190058.

Nagar, Yiftach. Combining human and machine intelligence for making predictions. Diss. Massachusetts Institute of Technology, 2013.

Mayo, Ray Cody, and Jessica Leung. "Artificial intelligence and deep learning–Radiology's next frontier ?." Clinical imaging 49 (2018): 87-88.

Curtis, Catherine, et al. "Machine learning for predicting patient wait times and appointment delays." Journal of the American College of Radiology 15.9 (2018): 1310-1316.

Syed, Ali B., and Adam C. Zoga. "Artificial intelligence in radiology: current technology and future directions." Seminars in musculoskeletal radiology. Vol. 22. No. 05. Thieme Medical Publishers, 2018.

Lehman, Constance D., et al. "Diagnostic accuracy of digital screening mammography with and without computer-aided detection." JAMA internal medicine 175.11 (2015): 1828-1837.

Ribli, Dezső, et al. "Detecting and classifying lesions in mammograms with deep learning." Scientific reports 8.1 (2018): 1-7.

Mohamed, Aly A., et al. "A deep learning method for classifying mammographic breast density categories." Medical physics 45.1 (2018): 314-321.

Bahl, Manisha, et al. "High-risk breast lesions: a machine learning model to predict pathologic upgrade and reduce unnecessary surgical excision." Radiology 286.3 (2018): 810-818.

Chartrand, Gabriel, et al. "Deep learning: a primer for radiologists." Radiographics 37.7 (2017): 2113-2131.

American Medical Association. "Augmented intelligence in health care (2018)." 1-8.


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