Imaging AI in Practice: A Demonstration of Future Workflow Using Integration Standards

Artificial intelligence in oncologic imaging

“…Aside from the need for rigorous clinical validation linked to patient outcomes, integration with current health IT systems and radiology workflow will also be a challenge. Radiology organizations such as the American College of Radiology (ACR) and Radiologic Society of North America (RSNA) have organized demonstrations to engage with relevant stakeholders and develop standards to facilitate this [67] . With ever increasing imaging volumes and workforce shortages, AI that increases the work time in a radiologist’s day will not be adopted.…”

Implementation of a Large-Scale Image Curation Workflow Using Deep Learning Framework

“…We are in an exciting era of AI implementation where integration standards are being defined. 17 In this paper, we describe our thought process and the factors involved in implementing AI in a workflow using an example of a multiclass classifier model and probability scores for identifying mislabeled retinal images. As AI-based research continues to advance in ophthalmology, we will need to invest more research into tools that foster trust.…”

Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges

“…With continued research and growing capital investment in the industry, AI has the potential to revolutionize the medical imaging industry by providing improved diagnostic accuracy, increased efficiency, reduced costs, and better patient outcomes. In contrast to the initial claims that AI would render radiologists obsolete, this outlook has recently been redefined into a new paradigm—that AI will augment the modern-day radiologist and facilitate higher-quality and more efficient patient care [ 2 , 3 ].…”