Katherine P. Andriole

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.

Artificial intelligence tools can be implemented throughout the radiologic imaging chain and best integrated into point-of-care systems using interoperability and semantic standards to ensure uniform data formats, communication protocols, and consistent interpretation across vendors and platforms.

“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review ...

Picture archiving and communication systems (PACS) enabled the electronic acquisition of radiological images, storage on digital media, transmission across communication networks, and visualization of medical images on computer display stations. Figure 1 shows the components of a PACS including image acquisition, archival, transmission, and display elements.

Through my engineering work, I was particularly interested in signal processing, and signals in computers are really just ones and zeros...

A machine learning algorithm was developed that can accurately segment and classify adrenal glands as normal or mass-containing on contrast-enhanced CT images, with performance similar to that of r... Background Adrenal masses are common, but radiology reporting and recommendations for management can be variable. Purpose To create a machine learning algorithm to segment adrenal glands on contras...

To commemorate the SPIE Medical Imaging 50th anniversary, this article provides a brief review of the Picture Archiving and Communication Systems (PACS) and Informatics conferences. Important topics and advances, contributing researchers from both academia and industry, and key papers are noted.

“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review ...