Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/26610
Title: Artificial intelligence in clinical decision support and outcome prediction - applications in stroke.
Austin Authors: Yeo, Melissa;Kok, Hong Kuan;Kutaiba, Numan ;Maingard, Julian;Thijs, Vincent N ;Tahayori, Bahman;Russell, Jeremy H ;Jhamb, Ashu;Chandra, Ronil V;Brooks, Duncan Mark ;Barras, Christen D;Asadi, Hamed 
Affiliation: Interventional Radiology Service, Department of Radiology, Northern Health, Melbourne, Victoria, Australia
School of Medicine, Faculty of Health, Deakin University, Burwood, Victoria, Australia
Interventional Neuroradiology Unit, Monash Health, Clayton, Victoria, Australia
Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
Department of Radiology, St Vincent's Hospital, Melbourne, Victoria, Australia
Radiology
School of Medicine, University of Melbourne, Melbourne, Victoria, Australia
Stroke Theme, Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
Neurology
Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
IBM Research Australia, Melbourne, Victoria, Australia
Neurosurgery
South Australian Institute of Health and Medical Research, Adelaide, South Australia, Australia
School of Medicine, The University of Adelaide, Adelaide, South Australia, Australia
Issue Date: 28-May-2021
Date: 2021-05-28
Publication information: Journal of Medical Imaging and Radiation Oncology 2021; online first: 28 May
Abstract: Artificial 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.
URI: https://ahro.austin.org.au/austinjspui/handle/1/26610
DOI: 10.1111/1754-9485.13193
ORCID: 0000-0001-5568-7303
0000-0003-4627-9847
0000-0003-1899-1909
0000-0003-2475-9727
Journal: Journal of Medical Imaging and Radiation Oncology
PubMed URL: 34050596
Type: Journal Article
Subjects: artificial intelligence
computer aided diagnosis
computers in radiology
decision support
machine learning
neuroradiology
outcome prediction
Stroke
Appears in Collections:Journal articles

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