Big data, machine learning and artificial intelligence: a neurologist’s guide

• Published in: Practical neurology Vol. 21; no. 1; pp. 4 - 11
• Main Authors: Auger, Stephen D, Jacobs, Benjamin M, Dobson, Ruth, Marshall, Charles R, Noyce, Alastair J
• Format: Journal Article
• Language: English
• Published: England BMJ Publishing Group Ltd 01.02.2021; BMJ Publishing Group LTD; BMJ Publishing Group
• Subjects: Algorithms; Artificial intelligence; Clinical medicine; clinical neurology; evidence-based neurology; health policy & practice; image analysis; Machine learning; Neural networks; Neurons; Neuroradiology; Review; health policy & practice; clinical neurology; image analysis; Neuroradiology; evidence-based neurology
• ISSN: 1474-7758; 1474-7766; 1474-7766
• DOI: 10.1136/practneurol-2020-002688

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.