Artificial intelligence in radiology: the ecosystem essential to improving patient care


      The rapid development of artificial intelligence (AI) has led to its widespread use in multiple industries, including healthcare. AI has the potential to be a transformative technology that will significantly impact patient care. Particularly, AI has a promising role in radiology, in which computers are indispensable and new technological advances are often sought out and adopted early in clinical practice. We present an overview of the basic definitions of common terms, the development of an AI ecosystem in imaging and its value in mitigating the challenges of implementation in clinical practice.


      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Clinical Imaging
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Bhargavan M
        • Sunshine JH
        Utilization of radiology services in the United States: levels and trends in modalities, regions, and populations.
        Radiology. 2005; 234: 824-832
        • Smith-Bindman R
        • Miglioretti DL
        • Larson EB
        Rising use of diagnostic medical imaging in a large integrated health system.
        Health Aff. 2008; 27: 1491-1502
        • Langlotz C.
        The radiology report.
        • Minksy M
        Steps toward artificial intelligence.
        Proc IRE. 1961; 49: 18-30
      1. Allen B, Gish R, Dreyer K. The role of an artificial intelligence ecosystem in radiology. Artif Intell Med Imaging. 2019: 291–327. doi:

        • Mazurowski MA
        • Buda M
        • Saha A
        • Bashir MR
        Deep learning in radiology: an overview of the concepts and a survey of the state of the art.
        153. 2018: 1-27
        • Gillies Robert J.
        • Kinahan Paul E.
        • Hricak Hedvig
        Radiomics: images are more than pictures, they are data.
        Radiology. 2015; 278: 563-577
        • Langlotz CP
        • Allen B
        • Erickson BJ
        • Kalpathy-Cramer J
        • Bigelow K
        • Cook TS
        • et al.
        A roadmap for foundational research on artificial intelligence in medical imaging: from the 2018 NIH/RSNA/ACR/The Academy Workshop. Adiology.
        190613. 2019 (Apr 16)
        • McGinty GB
        • Allen B
        The ACR data science institute and AI advisory group: harnessing the power of artificial intelligence to improve patient care.
        J Am Coll Radiol. 2018; 15: 577-579
        • Allen Jr, B
        • Seltzer SE
        • Langlotz CP
        • Dreyer KP
        • Summers RM
        • Petrick N
        • 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.
        J Am Coll Radiol. 2019; ([May 28])
        • Solenov D
        • Brieler J
        • Scherrer JF
        The potential of quantum computing and machine learning to advance clinical research and change the practice of medicine.
        Mo Med. 2018; 115: 463
        • Martin AB
        • Hartman M
        • Washington B
        • Catlin A
        • Team TNHEA
        National health care spending in 2017: growth slows to post–great recession rates; share of GDP stabilizes.
        Health Aff. 2019; 38
        • Office CB
        The federal budget in 2017: an infographic.
        Date accessed: February 28, 2019
      2. AI in healthcare Heatmap: from diagnostics to drug discovery startups, the category heats up. CB insights.
        • Ravi D
        • Wong C
        • Deligianni F
        • Berthelot M
        • Andreu-perez J
        • Lo B
        Deep Learning for Health Informatics. 2017; 21: 4-21
        • Pesapane F
        • Codari M
        • Sardanelli F
        Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine.
        Eur Radiol Exp. 2018; 2: 35
        • Allen B
        • Dreyer K
        The artificial intelligence ecosystem for the radiological sciences: ideas to clinical practice.
        J Am Coll Radiol. 2018; 15: 1455-1457
        • Fazal MI
        • Patel ME
        • Tye J
        • Gupta Y
        The past, present and future role of artificial intelligence in imaging.
        Eur J Radiol. 2018; 105: 246-250
        • Berwick DM
        • Nolan TW
        • Whittington J
        The triple aim: care, health, and cost.
        Health Aff. 2008; 27: 759-769
        • Bodenheimer T
        • Sinsky C
        From triple to quadruple aim: care of the patient requires care of the provider.
        Ann Fam Med. 2014; 12 (Nov 1): 573-576
        • Ellenbogen PH
        Imaging 3.0: what is it?.
        J Am Coll Radiol. 2013; 10: 229
        • Aminololama-Shakeri S
        • López JE
        The doctor-patient relationship with artificial intelligence.
        Am J Roentgenol. 2018; : 1-3
      3. Langlotz, C. P. (2019). Will artificial intelligence replace radiologists?. Radiology Artificial intelligence. 2019.

        • Nagar Y
        Combining human and machine intelligence for making predictions.
        in: MIT cent collect intell work pap. 2. 2011: 1-6
        • Mayo RC
        • Leung J
        Artificial intelligence and deep learning – radiology’s next frontier?.
        Clin Imaging. 2018; 49 (November 2017): 87-88
        • Curtis C
        • Liu C
        • Bollerman TJ
        • Pianykh OS
        Machine learning for predicting patient wait times and appointment delays.
        J Am Coll Radiol. 2018; 15: 1310-1316
        • Syed AB
        • Zoga AC
        Artificial intelligence in radiology: current technology and future directions.
        Semin Musculoskelet Radiol. 2018; 22: 540-545
        • Lehman CD
        • Wellman RD
        • Buist DSM
        • Kerlikowske K
        • Tosteson ANA
        • Miglioretti DL
        Diagnostic accuracy of digital screening mammography with and without computer-aided detection.
        JAMA Intern Med. 2015; 175: 1828-1837
        • Ribli D
        • Horvath A
        • Unger Z
        • Pollner P
        • Csabai I
        Detecting and classifying lesions in mammograms with deep learning.
        Sci Rep. 2018; 8: 4165
        • Mohamed AA
        • Berg WA
        • Peng H
        • Luo Y
        • Jankowitz RC
        • Wu S
        A deep learning method for classifying mammographic breast density categories.
        Med Phys. 2018; 45: 314-321
        • Bahl M
        • Barzilay R
        • Yedidia AB
        • Locascio NJ
        • Yu L
        • Lehman CD
        High-risk breast lesions: a machine learning model to predict pathologic upgrade and reduce unnecessary surgical excision.
        Radiology. 2018; 286: 810-818
        • Chartrand G
        • Cheng PM
        • Vorontsov E
        • et al.
        Deep learning: a primer for radiologists.
        Radio Graphics. 2017; 37: 2113-2131
        • American Medical Association
        Augmented intelligence in health care.
        2018: 1-8