Is AI the Ultimate QA?
We are among the many that believe that artificial intelligence will not replace practitioners and is most valuable as an adjunct in diagnostic radiology. We suggest a different approach to utilizing the technology, which may help even radiologists who may be averse to adopting AI. A novel method of...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 3; pp. 534 - 538 |
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| Autores principales: | , , , , |
| Formato: | tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2022
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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=157184687&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157184687 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2022 vid: 35 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 157184687 155260705 157184687 157184687 10.1007/s10278-022-00598-8 157184687 ppf: 534 ppct: 4 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Is AI the Ultimate QA? aug: au: Weisberg, Edmund M. Chu, Linda C. Nguyen, Benjamin D. Tran, Pelu Fishman, Elliot K. affil: The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medicine, 601 North Caroline Street, 21287, Baltimore, MD, USA sug: subj: Artificial Intelligence Quality Assurance Natural Language Processing Radiologists Computer-Aided Design Diagnosis, Computer Assisted Tomography, X-Ray Computed Quality Improvement Decision Making, Computer Assisted Algorithms ab: We are among the many that believe that artificial intelligence will not replace practitioners and is most valuable as an adjunct in diagnostic radiology. We suggest a different approach to utilizing the technology, which may help even radiologists who may be averse to adopting AI. A novel method of leveraging AI combines computer vision and natural language processing to ambiently function in the background, monitoring for critical care gaps. This AI Quality workflow uses a visual classifier to predict the likelihood of a finding of interest, such as a lung nodule, and then leverages natural language processing to review a radiologist's report, identifying discrepancies between imaging and documentation. Comparing artificial intelligence predictions with natural language processing report extractions with artificial intelligence in the background of computer-aided detection decisions may offer numerous potential benefits, including streamlined workflow, improved detection quality, an alternative approach to thinking of AI, and possibly even indemnity against malpractice. Here we consider early indications of the potential of artificial intelligence as the ultimate quality assurance for radiologists. pubtype: Academic Journal doctype: tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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