Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA.

Journal: Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Published Date: Jan 22, 2024

Abstract

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‑growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‑society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.

Authors

  • Adrian P Brady
    Mercy University Hospital, Cork, Ireland.
  • Bibb Allen
    Department of Radiology, Grandview Medical Center, Birmingham, Alabama. Electronic address: bibb@mac.com.
  • Jaron Chong
    Department of Radiology, McGill University Health Center, Montréal, Québec, Canada.
  • Elmar Kotter
    Department of Diagnostic and Interventional Radiology, Medical Center, University of Freiburg, Faculty of Medicine, Freiburg, Germany.
  • Nina Kottler
    Radiology Partners, El Segundo, California. Electronic address: nina.kottler@radpartners.com.
  • John Mongan
    From the Departments of Urology (T.C., M.U., H.C.C., M.S.) and Radiology and Biomedical Imaging (J.M., M.P.K., A.T., P.J., R.G., S.W.), University of California, San Francisco. 505 Parnassus Ave, M-391, San Francisco, CA 94143; and Division of Urology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, The Thai Red Cross Society, Bangkok, Thailand (M.U.).
  • Lauren Oakden-Rayner
    School of Public Health, University of Adelaide, Adelaide, SA, Australia; Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia. Electronic address: lauren.oakden-rayner@adelaide.edu.au.
  • Daniel Pinto Dos Santos
    Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
  • An Tang
    Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montreal, Canada.
  • Christoph Wald
    Chairman, Department of Radiology at Lahey Hospital & Medical Center, Professor of Radiology, Tufts University Medical School; Chair of the ACR Informatics Commission.
  • John Slavotinek
    South Australia Medical Imaging, Flinders Medical Centre Adelaide, Adelaide, Australia; College of Medicine and Public Health, Flinders University, Adelaide, Australia.

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