AI Integration in the Clinical Workflow.
Machine learning and artificial intelligence (AI) algorithms hold significant promise for addressing important clinical needs when applied to medical imaging; however, integration of algorithms into a radiology department is challenging. Vended algorithms are integrated into the workflow, successful...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 6; pp. 1435 - 1447 |
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| Autores principales: | , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154097228&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154097228 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2021 vid: 34 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 154097228 153146670 154097228 154097228 10.1007/s10278-021-00525-3 154097228 ppf: 1435 ppct: 12 formats: fmt: @attributes: type: P tig: atl: AI Integration in the Clinical Workflow. aug: au: Blezek, Daniel J. Olson-Williams, Lonny Missert, Andrew Korfiatis, Pangiotis affil: Mayo Clinic Rochester, 200 First Street SW, 55905, Rochester, MN, USA sug: subj: Workflow Artificial Intelligence Machine Learning Systems Integration Radiology Service Human Algorithms Digital Imaging Communication DICOM Feedback Radiologists Professional Role Body Composition Kidney, Cystic ab: Machine learning and artificial intelligence (AI) algorithms hold significant promise for addressing important clinical needs when applied to medical imaging; however, integration of algorithms into a radiology department is challenging. Vended algorithms are integrated into the workflow, successfully, but are typically closed systems and unavailable for site researchers to deploy algorithms. Rather than AI researchers creating one-off solutions, a general, multi-purpose integration system is desired. Here, we present a set of use cases and requirements for a system designed to enable rapid deployment of AI algorithms into the radiologist's workflow. The system uses standards-compliant digital imaging and communications in medicine structured reporting (DICOM SR) to present AI measurements, results, and findings to the radiologist in a clinical context and enables acceptance or rejection of results. The system also implements a feedback mechanism for post-processing technologists to correct results as directed by the radiologist. We demonstrate integration of a body composition algorithm and an algorithm for determining total kidney volume for patients with polycystic kidney disease. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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