Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice

Yasasvi Tadavarthi, Valeria Makeeva, William Wagstaff, Henry Zhan, Anna Podlasek, Neil Bhatia, Marta Heilbrun, Elizabeth Krupinski, Nabile Safdar, Imon Banerjee, Judy Gichoya, Hari Trivedi

Research output: Contribution to journalReview articlepeer-review

17 Citations (Scopus)

Abstract

Artificial intelligence has become a ubiquitous term in radiology over the past several years, and much attention has been given to applications that aid radiologists in the detection of abnormalities and diagnosis of diseases. However, there are many potential applications related to radiologic image quality, safety, and workflow improvements that present equal, if not greater, value propositions to radiology practices, insurance companies, and hospital systems. This review focuses on six major categories for artificial intelligence applications: study selection and protocoling, image acquisition, worklist prioritization, study reporting, business applications, and resident education. All of these categories can substantially affect different aspects of radiology practices and workflows. Each of these categories has different value propositions in terms of whether they could be used to increase efficiency, improve patient safety, increase revenue, or save costs. Each application is covered in depth in the context of both current and future areas of work.

Original languageEnglish
Article numbere210114
Number of pages12
JournalRadiology: Artificial Intelligence
Volume4
Issue number2
Early online date2 Feb 2022
DOIs
Publication statusPublished - Mar 2022

Keywords

  • Application Domain
  • Safety
  • Supervised Learning
  • Use of AI in Education

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Artificial Intelligence

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