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dc.contributor.authorYeo, Melissa-
dc.contributor.authorKok, Hong Kuan-
dc.contributor.authorKutaiba, Numan-
dc.contributor.authorMaingard, Julian-
dc.contributor.authorThijs, Vincent N-
dc.contributor.authorTahayori, Bahman-
dc.contributor.authorRussell, Jeremy H-
dc.contributor.authorJhamb, Ashu-
dc.contributor.authorChandra, Ronil V-
dc.contributor.authorBrooks, Duncan Mark-
dc.contributor.authorBarras, Christen D-
dc.contributor.authorAsadi, Hamed-
dc.identifier.citationJournal of Medical Imaging and Radiation Oncology 2021; online first: 28 Mayen
dc.description.abstractArtificial intelligence (AI) is making a profound impact in healthcare, with the number of AI applications in medicine increasing substantially over the past five years. In acute stroke, it is playing an increasingly important role in clinical decision-making. Contemporary advances have increased the amount of information - both clinical and radiological - which clinicians must consider when managing patients. In the time-critical setting of acute stroke, AI offers the tools to rapidly evaluate and consolidate available information, extracting specific predictions from rich, noisy data. It has been applied to the automatic detection of stroke lesions on imaging and can guide treatment decisions through the prediction of tissue outcomes and long-term functional outcomes. This review examines the current state of AI applications in stroke, exploring their potential to reform stroke care through clinical decision support, as well as the challenges and limitations which must be addressed to facilitate their acceptance and adoption for clinical use.en
dc.subjectartificial intelligenceen
dc.subjectcomputer aided diagnosisen
dc.subjectcomputers in radiologyen
dc.subjectdecision supporten
dc.subjectmachine learningen
dc.subjectoutcome predictionen
dc.titleArtificial intelligence in clinical decision support and outcome prediction - applications in stroke.en
dc.typeJournal Articleen
dc.identifier.journaltitleJournal of Medical Imaging and Radiation Oncologyen
dc.identifier.affiliationInterventional Radiology Service, Department of Radiology, Northern Health, Melbourne, Victoria, Australiaen
dc.identifier.affiliationSchool of Medicine, Faculty of Health, Deakin University, Burwood, Victoria, Australiaen
dc.identifier.affiliationInterventional Neuroradiology Unit, Monash Health, Clayton, Victoria, Australiaen
dc.identifier.affiliationFaculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australiaen
dc.identifier.affiliationDepartment of Radiology, St Vincent's Hospital, Melbourne, Victoria, Australiaen
dc.identifier.affiliationSchool of Medicine, University of Melbourne, Melbourne, Victoria, Australiaen
dc.identifier.affiliationStroke Theme, Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australiaen
dc.identifier.affiliationDepartment of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australiaen
dc.identifier.affiliationIBM Research Australia, Melbourne, Victoria, Australiaen
dc.identifier.affiliationSouth Australian Institute of Health and Medical Research, Adelaide, South Australia, Australiaen
dc.identifier.affiliationSchool of Medicine, The University of Adelaide, Adelaide, South Australia, Australiaen
dc.identifier.pubmedid34050596, Hamed
item.fulltextNo Fulltext-
item.openairetypeJournal Article-
item.languageiso639-1en- Florey Institute of Neuroscience and Mental Health-
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