Optimization of Radiology Workflow with Artificial Intelligence.

Erik Ranschaert, Laurens Topff, Oleg Pianykh
Author Information
  1. Erik Ranschaert: Elisabeth-Tweesteden Hospital, Hilvarenbeekseweg 60, 5022 GC Tilburg, The Netherlands; Ghent University, C. Heymanslaan 10, 9000 Gent, Belgium. Electronic address: erik.ranschaert@ugent.be.
  2. Laurens Topff: Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
  3. Oleg Pianykh: Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 25 New Chardon Street, Suite 470, Boston, MA 02114, USA.

Abstract

The potential of artificial intelligence (AI) in radiology goes far beyond image analysis. AI can be used to optimize all steps of the radiology workflow by supporting a variety of nondiagnostic tasks, including order entry support, patient scheduling, resource allocation, and improving the radiologist's workflow. This article discusses several principal directions of using AI algorithms to improve radiological operations and workflow management, with the intention of providing a broader understanding of the value of applying AI in the radiology department.

Keywords

MeSH Term

Artificial Intelligence
Diagnostic Imaging
Humans
Image Interpretation, Computer-Assisted
Radiology
Workflow

Word Cloud

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