Artificial intelligence for clinical decision support in neurology. Issue 2 (9th July 2020)

• Record Type: Journal Article
• Authors: Pedersen, Mangor; Verspoor, Karin; Jenkinson, Mark; Law, Meng; Abbott, David F; Jackson, Graeme D
• Is Part Of: Brain communications. Volume 2:Issue 2(2020)
• Publication Date: 2020-07-09
• Subjects: artificial intelligence -- neurology -- augmented intelligence -- deep learning -- ethics; 616
• DOI: 10.1093/braincomms/fcaa096
• Languages: English
• ISSNs: 2632-1297

Abstract

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. Abstract : Artificial intelligence can transform healthcare as we know it, to a system where humans and machines work together to facilitating expert-level clinical decision tools across healthcare settings. We review contemporary deep-learning methods and ethical principles in artificial intelligence, as we are moving into the future of precision medicine. Graphical Abstract: