An Interventional Radiologist's Primer of Critical Appraisal of Artificial Intelligence Research.

Journal: Journal of vascular and interventional radiology : JVIR
Published Date:

Abstract

Recent advances in artificial intelligence (AI) are expected to cause a significant paradigm shift in all digital data-driven aspects of information gain, processing, and decision making in both clinical healthcare and medical research. The field of interventional radiology (IR) will be enmeshed in this innovation, yet the collective IR expertise in the field of AI remains rudimentary because of lack of training. This primer provides the clinical interventional radiologist with a simple guide for critically appraising AI research and products by identifying 12 fundamental items that should be considered: (a) need for AI technology to address the clinical problem, (b) type of applied Al algorithm, (c) data quality and degree of annotation, (d) reporting of accuracy, (e) applicability of standardized reporting, (f) reproducibility of methodology and data transparency, (g) algorithm validation, (h) interpretability, (i) concrete impact on IR, (j) pathway toward translation to clinical practice, (k) clinical benefit and cost-effectiveness, and (l) regulatory framework.

Authors

  • Olivia Gaddum
    Division of Interventional Radiology, Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut.
  • Julius Chapiro
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06520. Electronic address: julius.chapiro@yale.edu.