TY - JOUR
T1 - Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice
AU - Tadavarthi, Yasasvi
AU - Makeeva, Valeria
AU - Wagstaff, William
AU - Zhan, Henry
AU - Podlasek, Anna
AU - Bhatia, Neil
AU - Heilbrun, Marta
AU - Krupinski, Elizabeth
AU - Safdar, Nabile
AU - Banerjee, Imon
AU - Gichoya, Judy
AU - Trivedi, Hari
N1 - Publisher Copyright:
© RSNA, 2022.
PY - 2022/3
Y1 - 2022/3
N2 - 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.
AB - 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.
KW - Application Domain
KW - Safety
KW - Supervised Learning
KW - Use of AI in Education
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U2 - 10.1148/RYAI.210114
DO - 10.1148/RYAI.210114
M3 - Review article
AN - SCOPUS:85128344434
SN - 2638-6100
VL - 4
JO - Radiology: Artificial Intelligence
JF - Radiology: Artificial Intelligence
IS - 2
M1 - e210114
ER -