A Cloud-Based System for Automated AI Image Analysis and Reporting
Although numerous AI algorithms have been published, the relatively small number of algorithms used clinically is partly due to the difficulty of implementing AI seamlessly into the clinical workflow for radiologists and for their healthcare enterprise. The authors developed an AI orchestrator to fa...
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Veröffentlicht in: | Journal of imaging informatics in medicine 2024-07 |
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creator | Chatterjee, Neil Duda, Jeffrey Gee, James Elahi, Ameena Martin, Kristen Doan, Van Liu, Hannah Maclean, Matthew Rader, Daniel Borthakur, Arijitt Kahn, Charles Sagreiya, Hersh Witschey, Walter |
description | Although numerous AI algorithms have been published, the relatively small number of algorithms used clinically is partly due to the difficulty of implementing AI seamlessly into the clinical workflow for radiologists and for their healthcare enterprise. The authors developed an AI orchestrator to facilitate the deployment and use of AI tools in a large multi-site university healthcare system and used it to conduct opportunistic screening for hepatic steatosis. During the 60-day study period, 991 abdominal CTs were processed at multiple different physical locations with an average turnaround time of 2.8 min. Quality control images and AI results were fully integrated into the existing clinical workflow. All input into and output from the server was in standardized data formats. The authors describe the methodology in detail; this framework can be adapted to integrate any clinical AI algorithm. |
doi_str_mv | 10.1007/s10278-024-01200-z |
format | Article |
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title | A Cloud-Based System for Automated AI Image Analysis and Reporting |
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