Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology.
Publication/Presentation Date
1-1-2024
Abstract
Despite recent advancements in machine learning (ML) applications in health care, there have been few benefits and improvements to clinical medicine in the hospital setting. To facilitate clinical adaptation of methods in ML, this review proposes a standardized framework for the step-by-step implementation of artificial intelligence into the clinical practice of radiology that focuses on three key components: problem identification, stakeholder alignment, and pipeline integration. A review of the recent literature and empirical evidence in radiologic imaging applications justifies this approach and offers a discussion on structuring implementation efforts to help other hospital practices leverage ML to improve patient care. Clinical trial registration no. 04242667 © RSNA, 2024
Volume
310
Issue
1
First Page
223170
Last Page
223170
ISSN
1527-1315
Published In/Presented At
Chae, A., Yao, M. S., Sagreiya, H., Goldberg, A. D., Chatterjee, N., MacLean, M. T., Duda, J., Elahi, A., Borthakur, A., Ritchie, M. D., Rader, D., Kahn, C. E., Witschey, W. R., & Gee, J. C. (2024). Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology. Radiology, 310(1), e223170. https://doi.org/10.1148/radiol.223170
Disciplines
Diagnosis | Medicine and Health Sciences | Other Analytical, Diagnostic and Therapeutic Techniques and Equipment | Radiology
PubMedID
38259208
Department(s)
Department of Radiology and Diagnostic Medical Imaging
Document Type
Article