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    Artificial intelligence for clinical decision support in neurology.

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    Author
    Pedersen, M; Verspoor, K; Jenkinson, M; Law, M; Abbott, DF; Jackson, GD
    Date
    2020
    Source Title
    Brain Communications
    Publisher
    Oxford University Press (OUP)
    University of Melbourne Author/s
    Abbott, David; Jenkinson, Mark; Jackson, Graeme; Verspoor, Cornelia
    Affiliation
    Medicine and Radiology
    Centre for Neuroscience
    Florey Department of Neuroscience and Mental Health
    Computing and Information Systems
    Metadata
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    Document Type
    Journal Article
    Citations
    Pedersen, M., Verspoor, K., Jenkinson, M., Law, M., Abbott, D. F. & Jackson, G. D. (2020). Artificial intelligence for clinical decision support in neurology.. Brain Commun, 2 (2), pp.fcaa096-. https://doi.org/10.1093/braincomms/fcaa096.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/251505
    DOI
    10.1093/braincomms/fcaa096
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7585692
    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.

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    • Minerva Elements Records [52443]
    • Computing and Information Systems - Research Publications [1565]
    • Florey Department of Neuroscience and Mental Health - Research Publications [1312]
    • Centre for Neuroscience - Research Publications [92]
    • Medicine and Radiology - Research Publications [3310]
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