Optimization of Radiology Workflow with Artificial Intelligence

Erik Ranschaert; Laurens Topff; Oleg Pianykh

doi:10.1016/j.rcl.2021.06.006

Optimization of Radiology Workflow with Artificial Intelligence

Radiol Clin North Am. 2021 Nov;59(6):955-966. doi: 10.1016/j.rcl.2021.06.006.

Authors

Erik Ranschaert¹, Laurens Topff², Oleg Pianykh³

Affiliations

¹ 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 Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
³ Department of Radiology, Harvard Medical School, Massachusetts General Hospital, 25 New Chardon Street, Suite 470, Boston, MA 02114, USA.

PMID: 34689880
DOI: 10.1016/j.rcl.2021.06.006

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: Artificial intelligence; Machine learning; Operational efficiency; Radiology; Workflow.

Publication types

Review

MeSH terms

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