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Journal of Neurology

, Volume 265, Issue 11, pp 2745–2748 | Cite as

Machine learning in neurology: what neurologists can learn from machines and vice versa

  • Rose Bruffaerts
Neurological Update

Abstract

Artificial intelligence is increasingly becoming a part of everyday life. This raises the question whether clinical neurology can benefit from these novel methods to increase diagnostic accuracy. Several recent studies have used machine learning classifiers to predict whether subjects suffer from a neurological disorder. This article discusses whether these methods are ready to make their entrance into clinical practice. The underlying principles of classification will be explored, as well as the potential pitfalls. Strengths of machine learning methods are that they are unbiased and very sensitive to patterns emerging from small changes spread across a large number of variables. Potential pitfalls are that building reliable classifiers requires large amounts of well-selected data and extensive validation. Currently, machine learning classifiers offer neurologists a new diagnostic tool which can aid in the diagnosis of cases with a high degree of uncertainty.

Keywords

Artificial intelligence Machine learning Support vector machines Diagnostic accuracy Classification 

Notes

Acknowledgements

The author thanks Bruno Bergmans, M.D., Ph.D., for useful comments on prior versions of this paper.

Funding

RB is a postdoctoral fellow of the Research Foundation Flanders (F.W.O.)

Compliance with ethical standards

Conflicts of interest

Dr. Bruffaerts reports no disclosures.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratory for Cognitive Neurology, Department of NeurosciencesKU LeuvenLeuvenBelgium
  2. 2.Neurology DepartmentUniversity Hospitals LeuvenLeuvenBelgium

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