Workflow Integration of Research AI Tools into a Hospital Radiology Rapid Prototyping Environment.
The field of artificial intelligence (AI) in medical imaging is undergoing explosive growth, and Radiology is a prime target for innovation. The American College of Radiology Data Science Institute has identified more than 240 specific use cases where AI could be used to improve clinical practice. I...
| Published in: | Journal of Digital Imaging Vol. 35; no. 4; pp. 1023 - 1034 |
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| Main Authors: | , , , , , , , , , , , , , |
| Format: | pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Aug2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159195614&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159195614 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2022 vid: 35 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159195614 155651748 159195614 159195614 10.1007/s10278-022-00601-2 159195614 ppf: 1023 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Workflow Integration of Research AI Tools into a Hospital Radiology Rapid Prototyping Environment. aug: au: Kanakaraj, Praitayini Ramadass, Karthik Bao, Shunxing Basford, Melissa Jones, Laura M. Lee, Ho Hin Xu, Kaiwen Schilling, Kurt G. Carr, John Jeffrey Terry, James Gregory Huo, Yuankai Sandler, Kim Lori Netwon, Allen T. Landman, Bennett A. affil: Computer Science, Vanderbilt University, Nashville, TN, USA sug: subj: Radiology Service Workflow Artificial Intelligence Picture Archiving and Communication Systems Systems Design Case Studies Health Insurance Portability and Accountability Act World Wide Web User-Computer Interface Diffusion of Innovation Privacy and Confidentiality ab: The field of artificial intelligence (AI) in medical imaging is undergoing explosive growth, and Radiology is a prime target for innovation. The American College of Radiology Data Science Institute has identified more than 240 specific use cases where AI could be used to improve clinical practice. In this context, thousands of potential methods are developed by research labs and industry innovators. Deploying AI tools within a clinical enterprise, even on limited retrospective evaluation, is complicated by security and privacy concerns. Thus, innovation must be weighed against the substantive resources required for local clinical evaluation. To reduce barriers to AI validation while maintaining rigorous security and privacy standards, we developed the AI Imaging Incubator. The AI Imaging Incubator serves as a DICOM storage destination within a clinical enterprise where images can be directed for novel research evaluation under Institutional Review Board approval. AI Imaging Incubator is controlled by a secure HIPAA-compliant front end and provides access to a menu of AI procedures captured within network-isolated containers. Results are served via a secure website that supports research and clinical data formats. Deployment of new AI approaches within this system is streamlined through a standardized application programming interface. This manuscript presents case studies of the AI Imaging Incubator applied to randomizing lung biopsies on chest CT, liver fat assessment on abdomen CT, and brain volumetry on head MRI. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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