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A Cloud-Based System for Automated AI Image Analysis and Reporting.
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.
Affiliation
  • Chatterjee N; Department of Radiology, University of Pennsylvania, Philadelphia, USA. nchatter@nm.org.
  • Duda J; Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, USA. nchatter@nm.org.
  • Gee J; Department of Radiology, University of Pennsylvania, Philadelphia, USA.
  • Elahi A; Department of Radiology, University of Pennsylvania, Philadelphia, USA.
  • Martin K; Perlman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Doan V; Department of Information Services, University of Pennsylvania, Philadelphia, USA.
  • Liu H; Department of Information Services, University of Pennsylvania, Philadelphia, USA.
  • Maclean M; Department of Information Services, University of Pennsylvania, Philadelphia, USA.
  • Rader D; Department of Bioengineering, University of Pennsylvania, Philadelphia, USA.
  • Borthakur A; Department of Radiology, University of Pennsylvania, Philadelphia, USA.
  • Kahn C; Department of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Sagreiya H; Department of Radiology, University of Pennsylvania, Philadelphia, USA.
  • Witschey W; Department of Radiology, University of Pennsylvania, Philadelphia, USA.
J Imaging Inform Med ; 2024 Jul 31.
Article in En | MEDLINE | ID: mdl-39085717
ABSTRACT
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.
Key words

Full text: 1 Database: MEDLINE Language: En Journal: J Imaging Inform Med Year: 2024 Type: Article Affiliation country: United States

Full text: 1 Database: MEDLINE Language: En Journal: J Imaging Inform Med Year: 2024 Type: Article Affiliation country: United States