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

Walter F. Wiggins, Kirti Magudia, Teri M. Sippel Schmidt, Stacy D. O’Connor, Christopher D. Carr, Marc Kohli, Katherine P. Andriole

Radiology Artificial Intelligence · 2021 · 40 citations · 1 references

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TL;DR

Artificial intelligence tools are rapidly being developed for radiology and other clinical areas, but they must be integrated into existing radiology systems to be usable and function as intended. The Imaging AI in Practice demonstrations were created by the Radiological Society of North America, radiologists, and imaging‑focused vendors to show how AI tools can generate, consume, and present results throughout the radiology workflow in a simulated clinical environment. These demonstrations underscore the critical importance of semantic and interoperability standards, as well as orchestration profiles, for successful clinical integration of radiology AI tools. Keywords: Computer Applications‑General (Informatics), Technology Assessment © RSNA, 2021.

Abstract

Artificial intelligence (AI) tools are rapidly being developed for radiology and other clinical areas. These tools have the potential to dramatically change clinical practice; however, for these tools to be usable and function as intended, they must be integrated into existing radiology systems. In a collaborative effort between the Radiological Society of North America, radiologists, and imaging-focused vendors, the Imaging AI in Practice (IAIP) demonstrations were developed to show how AI tools can generate, consume, and present results throughout the radiology workflow in a simulated clinical environment. The IAIP demonstrations highlight the critical importance of semantic and interoperability standards, as well as orchestration profiles for successful clinical integration of radiology AI tools. Keywords: Computer Applications-General (Informatics), Technology Assessment © RSNA, 2021.

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