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

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

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.

Citation

Tadavarthi, Y., Makeeva, V., Wagstaff, W., Zhan, H., Podlasek, A., Bhatia, N., …Trivedi, H. (2022). Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice. Radiology: Artificial Intelligence, 4(2), Article e210114. https://doi.org/10.1148/ryai.210114

• Journal Article Type: Article
• Acceptance Date: Jan 11, 2022
• Online Publication Date: Feb 2, 2022
• Publication Date: Mar 1, 2022
• Deposit Date: Jun 9, 2023
• Publicly Available Date: Jun 14, 2023
• Journal: Radiology: Artificial Intelligence
• Print ISSN: 2638-6100
• Electronic ISSN: 2638-6100
• Publisher: Radiological Society of North America
• Peer Reviewed: Peer Reviewed
• Volume: 4
• Issue: 2
• Article Number: e210114
• DOI: https://doi.org/10.1148/ryai.210114
• Keywords: Artificial Intelligence; Radiology, Nuclear Medicine and imaging; Radiological and Ultrasound Technology
• Public URL: https://nottingham-repository.worktribe.com/output/21646243
• Publisher URL: https://pubs.rsna.org/doi/10.1148/ryai.210114