AI for Radiology: A Primer Part II. Interacting with AI Results.
Authors
Affiliations (10)
Affiliations (10)
- Department of Radiology & Biomedical Imaging and Center for Intelligent Imaging, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143.
- Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, Mass.
- AI Office, Mass General Brigham, Boston, Mass.
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa.
- Techie Maestro, Waterloo, Ontario, Canada.
- Radiology Partners, Palmer Lake, Colo.
- Department of Applied Innovation and AI, Diagnósticos da América, São Paulo, Brazil.
- Department of Diagnostic Imaging, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil.
- Department of Radiology, Duke University School of Medicine, Durham, NC.
- Canon Medical Research USA, Vernon Hills, Ill.
Abstract
As artificial intelligence (AI) tools are increasingly integrated into imaging workflows, understanding how AI results are generated and presented to the end user can equip radiologists to optimize interactions with AI results in practice. Although AI solutions are marketed as high-performing options that promise efficiency and diagnostic gains, issues arising at the radiologist-AI interface can lead to diminished returns due to unintentional cognitive burdens or misalignments with clinical workflows. A foundational understanding of how images are processed by AI solutions, presented in imaging workflows, and documented can allow radiologists to troubleshoot shortcomings in practice after clinical deployment. This article is the second in a primer series providing a foundation in AI literacy for radiologists. Building on the first article in this primer series, this article addresses questions raised by end users while using AI in practice. It covers how to consider the intended use of AI solutions, the process through which AI results are generated, the reasons why results may not be available at the time of study interpretation, and how to align the presentation of AI results with end user workflows. The discussion also explores emerging topics and challenges, including considerations for storing AI results and the related medicolegal considerations.