Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi‐society statement from the ACR, CAR, ESR, RANZCR & RSNA

Author:

Brady Adrian P1,Allen Bibb23,Chong Jaron4,Kotter Elmar5,Kottler Nina67,Mongan John8,Oakden‐Rayner Lauren9ORCID,Pinto dos Santos Daniel1011,Tang An12,Wald Christoph131415,Slavotinek John1617

Affiliation:

1. University College Cork Cork Ireland

2. Department of Radiology Grandview Medical Center Birmingham Alabama USA

3. American College of Radiology Data Science Institute Reston Virginia USA

4. Department of Medical Imaging Schulich School of Medicine and Dentistry, Western University London Ontario Canada

5. Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine University of Freiburg Freiburg Germany

6. Radiology Partners El Segundo California USA

7. Stanford Center for Artificial Intelligence in Medicine & Imaging Palo Alto California USA

8. Department of Radiology and Biomedical Imaging University of California, San Francisco San Francisco California USA

9. Australian Institute for Machine Learning University of Adelaide Adelaide South Australia Australia

10. Department of Radiology University Hospital of Cologne Cologne Germany

11. Department of Radiology University Hospital of Frankfurt Frankfurt Germany

12. Department of Radiology, Radiation Oncology, and Nuclear Medicine Université de Montréal Montreal Quebec Canada

13. Department of Radiology Lahey Hospital & Medical Center Burlington Massachusetts USA

14. Tufts University Medical School Boston Massachusetts USA

15. Commision On Informatics, and Member, Board of Chancellors American College of Radiology Reston Virginia USA

16. South Australia Medical Imaging, Flinders Medical Centre Adelaide Adelaide South Australia Australia

17. College of Medicine and Public Health Flinders University Adelaide South Australia Australia

Abstract

SummaryArtificial 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.

Publisher

Wiley

Subject

Radiology, Nuclear Medicine and imaging,Oncology

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