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

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

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