| Literature DB >> 32994368 |
Stephen D Auger1, Benjamin M Jacobs2,3, Ruth Dobson2,3, Charles R Marshall2,3, Alastair J Noyce2,3.
Abstract
Modern clinical practice requires the integration and interpretation of ever-expanding volumes of clinical data. There is, therefore, an imperative to develop efficient ways to process and understand these large amounts of data. Neurologists work to understand the function of biological neural networks, but artificial neural networks and other forms of machine learning algorithm are likely to be increasingly encountered in clinical practice. As their use increases, clinicians will need to understand the basic principles and common types of algorithm. We aim to provide a coherent introduction to this jargon-heavy subject and equip neurologists with the tools to understand, critically appraise and apply insights from this burgeoning field. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.Entities:
Keywords: Neuroradiology; clinical neurology; evidence-based neurology; health policy & practice; image analysis
Year: 2020 PMID: 32994368 PMCID: PMC7841474 DOI: 10.1136/practneurol-2020-002688
Source DB: PubMed Journal: Pract Neurol ISSN: 1474-7758