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Title: Artificial intelligence for clinical decision support in neurology.
Austin Authors: Pedersen, Mangor;Verspoor, Karin;Jenkinson, Mark;Law, Meng;Abbott, David F ;Jackson, Graeme D 
Affiliation: Department of Psychology, Auckland University of Technology (AUT), Auckland, 0627, New Zealand
Department of Neuroscience, Monash School of Medicine, Nursing and Health Sciences, Melbourne, VIC 3181, Australia
Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia
Australian Institute for Machine Learning (AIML), The University of Adelaide, Adelaide, SA 5000, Australia
Department of Medicine Austin Health, The University of Melbourne, Heidelberg, VIC 3084, Australia
Department of Radiology, Alfred Hospital, Melbourne, VIC 3181, Australia
Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC 3181, Australia
School of Computing and Information Systems, The University of Melbourne, Parkville, VIC 3010, Australia
The Florey Institute of Neuroscience and Mental Health
Issue Date: 9-Jul-2020
Date: 2020-07-09
Publication information: Brain Communications 2020; 2(2): fcaa096
Abstract: Artificial intelligence is one of the most exciting methodological shifts in our era. It holds the potential to transform healthcare as we know it, to a system where humans and machines work together to provide better treatment for our patients. It is now clear that cutting edge artificial intelligence models in conjunction with high-quality clinical data will lead to improved prognostic and diagnostic models in neurological disease, facilitating expert-level clinical decision tools across healthcare settings. Despite the clinical promise of artificial intelligence, machine and deep-learning algorithms are not a one-size-fits-all solution for all types of clinical data and questions. In this article, we provide an overview of the core concepts of artificial intelligence, particularly contemporary deep-learning methods, to give clinician and neuroscience researchers an appreciation of how artificial intelligence can be harnessed to support clinical decisions. We clarify and emphasize the data quality and the human expertise needed to build robust clinical artificial intelligence models in neurology. As artificial intelligence is a rapidly evolving field, we take the opportunity to iterate important ethical principles to guide the field of medicine is it moves into an artificial intelligence enhanced future.
DOI: 10.1093/braincomms/fcaa096
ORCID: 0000-0002-9199-1916
Journal: Brain Communications
PubMed URL: 33134913
Type: Journal Article
Subjects: artificial intelligence
augmented intelligence
deep learning
Appears in Collections:Journal articles

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