2020 ACR Data Science Institute Artificial Intelligence Survey

Published:April 20, 2021DOI:



      The ACR Data Science Institute conducted its first annual survey of ACR members to understand how radiologists are using artificial intelligence (AI) in clinical practice and to provide a baseline for monitoring trends in AI use over time.


      The ACR Data Science Institute sent a brief electronic survey to all ACR members via email. Invitees were asked for demographic information about their practice and if and how they were currently using AI as part of their clinical work. They were also asked to evaluate the performance of AI models in their practices and to assess future needs.


      Approximately 30% of radiologists are currently using AI as part of their practice. Large practices were more likely to use AI than smaller ones, and of those using AI in clinical practice, most were using AI to enhance interpretation, most commonly detection of intracranial hemorrhage, pulmonary emboli, and mammographic abnormalities. Of practices not currently using AI, 20% plan to purchase AI tools in the next 1 to 5 years.


      The survey results indicate a modest penetrance of AI in clinical practice. Information from the survey will help researchers and industry develop AI tools that will enhance radiological practice and improve quality and efficiency in patient care.

      Key Words

      To read this article in full you will need to make a payment
      ACR Member Login
      ACR Members, access to JACR is a member benefit. Use your ACR credentials to access all JACR articles and features.
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


      1. Available at: Accessed Apri 20, 2021.

      2. ACR Data Science Institute. FDA-cleared AI algorithms. Available at: Accessed April 20, 2021.

        • European Society of Radiology
        Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology.
        Insights Imaging. 2019; 10: 105
        • Allen Jr., B.
        • Seltzer S.E.
        • Langlotz C.P.
        • 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; 16: 1179-1189
      3. ACR Data Science Institute. Define-AI directory. Available at: Accessed April 20, 2021.

        • Allen B.
        • Agarwal S.
        • Kalpathy-Cramer J.
        • Dreyer K.
        • Democratizing A.I.
        J Am Coll Radiol. 2019; 16: 961-963
      4. National Radiology Data Registry. Assess-AI. Available at: Accessed April 20, 2021.

        • Bender C.E.
        • Bansal S.
        • Wolfman D.
        • Parikh J.R.
        2019 ACR Commission on Human Resources workforce survey.
        J Am Coll Radiol. 2020; 17: 673-675