A Cloud-Based System for Automated AI Image Analysis and Reporting

• Published in: Journal of digital imaging Vol. 38; no. 1; pp. 368 - 379
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.02.2025; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Automation; Cloud Computing; Fatty liver; Fatty Liver - diagnostic imaging; Health care; Humans; Image analysis; Image processing; Image Processing, Computer-Assisted - methods; Image quality; Imaging; Informatics; Information technology; Medical imaging; Medicine; Medicine & Public Health; Patients; Quality control; Radiology; Software; Steatosis; Tomography, X-Ray Computed; Workflow; AI; AI orchestrator; Informatics; Opportunistic screening; Steatosis
• ISSN: 2948-2933; 0897-1889; 2948-2925; 2948-2933; 1618-727X
• DOI: 10.1007/s10278-024-01200-z

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.