Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology
Allison Chae1, Michael S Yao1, Hersh Sagreiya1
1From the Departments of Bioengineering (M.S.Y.), Radiology (H.S., N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K., W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104; Department of Radiology, Loyola University Medical Center, Maywood, Ill (A.D.G.); Department of Information Services, University of Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pa (A.B.).
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 Supplemental material is available for this article.
