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Artificial Intelligence for the Future Radiology Diagnostic Service

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dc.contributor.authorMun, Seong K.en
dc.contributor.authorWong, Kenneth H.en
dc.contributor.authorLo, Shih-Chung B.en
dc.contributor.authorLi, Yannien
dc.contributor.authorBayarsaikhan, Shijiren
dc.contributor.departmentArlington Innovation Center: Health Researchen
dc.date.accessioned2021-03-02T19:02:00Zen
dc.date.available2021-03-02T19:02:00Zen
dc.date.issued2021-01-28en
dc.description.abstractRadiology historically has been a leader of digital transformation in healthcare. The introduction of digital imaging systems, picture archiving and communication systems (PACS), and teleradiology transformed radiology services over the past 30 years. Radiology is again at the crossroad for the next generation of transformation, possibly evolving as a one-stop integrated diagnostic service. Artificial intelligence and machine learning promise to offer radiology new powerful new digital tools to facilitate the next transformation. The radiology community has been developing computer-aided diagnosis (CAD) tools based on machine learning (ML) over the past 20 years. Among various AI techniques, deep-learning convolutional neural networks (CNN) and its variants have been widely used in medical image pattern recognition. Since the 1990s, many CAD tools and products have been developed. However, clinical adoption has been slow due to a lack of substantial clinical advantages, difficulties integrating into existing workflow, and uncertain business models. This paper proposes three pathways for AI’s role in radiology beyond current CNN based capabilities 1) improve the performance of CAD, 2) improve the productivity of radiology service by AI-assisted workflow, and 3) develop radiomics that integrate the data from radiology, pathology, and genomics to facilitate the emergence of a new integrated diagnostic service.en
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMun SK, Wong KH, Lo S-CB, Li Y and Bayarsaikhan S (2021) Artificial Intelligence for the Future Radiology Diagnostic Service. Front. Mol. Biosci. 7:614258. doi: 10.3389/fmolb.2020.614258en
dc.identifier.doihttps://doi.org/10.3389/fmolb.2020.614258en
dc.identifier.urihttp://hdl.handle.net/10919/102510en
dc.identifier.volume7en
dc.language.isoenen
dc.publisherFrontiers Mediaen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectartificial intelligenceen
dc.subjectradiologyen
dc.subjectCNNen
dc.subjectproductivityen
dc.subjectintegrated diagnosticsen
dc.subjectworkflowen
dc.titleArtificial Intelligence for the Future Radiology Diagnostic Serviceen
dc.title.serialFrontiers in Molecular Biosciencesen
dc.typeArticle - Refereeden

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