An Orchestration Platform that Puts Radiologists in the Driver's Seat of AI Innovation: a Methodological Approach.
Current AI-driven research in radiology requires resources and expertise that are often inaccessible to small and resource-limited labs. The clinicians who are able to participate in AI research are frequently well-funded, well-staffed, and either have significant experience with AI and computing, o...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 2; pp. 700 - 715 |
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| Autores principales: | , |
| Formato: | algorithm diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2023
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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=162679395&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162679395 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2023 vid: 36 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 162679395 160373847 162679395 162679395 10.1007/s10278-022-00649-0 162679395 ppf: 700 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Orchestration Platform that Puts Radiologists in the Driver's Seat of AI Innovation: a Methodological Approach. aug: au: Cohen, Raphael Y. Sodickson, Aaron D. affil: Department of Radiology, Division of Emergency Radiology, Brigham and Women's Hospital, 02115, Boston, USA sug: subj: Radiologists Artificial Intelligence Diffusion of Innovation Diagnostic Imaging Architecture Program Implementation Human Female Male Methodological Research Cloud Computing Data Management Machine Learning Clinical Information Systems Data Analysis Software Female Male ab: Current AI-driven research in radiology requires resources and expertise that are often inaccessible to small and resource-limited labs. The clinicians who are able to participate in AI research are frequently well-funded, well-staffed, and either have significant experience with AI and computing, or have access to colleagues or facilities that do. Current imaging data is clinician-oriented and is not easily amenable to machine learning initiatives, resulting in inefficient, time consuming, and costly efforts that rely upon a crew of data engineers and machine learning scientists, and all too often preclude radiologists from driving AI research and innovation. We present the system and methodology we have developed to address infrastructure and platform needs, while reducing the staffing and resource barriers to entry. We emphasize a data-first and modular approach that streamlines the AI development and deployment process while providing efficient and familiar interfaces for radiologists, such that they can be the drivers of new AI innovations. pubtype: Academic Journal doctype: algorithm diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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