Allison Chae, Michael S Yao, Hersh Sagreiya, Ari D Goldberg, Neil Chatterjee, Matthew T MacLean, Jeffrey Duda, Ameena Elahi, Arijitt Borthakur, Marylyn D Ritchie, Daniel Rader, Charles E Kahn, Walter R Witschey, James C Gee
Radiology . 2024 Jan;310(1):e223170. doi: 10.1148/radiol.223170.
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