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

Stephen D Auger1, Benjamin M Jacobs2,3, Ruth Dobson2,3

  • 1Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, UK stephen.auger1@nhs.net.

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Summary

Artificial neural networks and machine learning algorithms are becoming crucial in clinical practice. This guide helps neurologists understand and apply these data processing tools for better patient care.

Keywords:
Neuroradiologyclinical neurologyevidence-based neurologyhealth policy & practiceimage analysis

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Area of Science:

  • Neuroscience
  • Computer Science
  • Clinical Informatics

Background:

  • Clinical practice generates vast amounts of data requiring efficient interpretation.
  • Neurologists study biological neural networks, but artificial intelligence is increasingly relevant.
  • Understanding machine learning algorithms is essential for modern healthcare.

Purpose of the Study:

  • To provide a clear introduction to artificial neural networks and machine learning for neurologists.
  • To equip clinicians with the knowledge to critically evaluate and utilize AI in practice.
  • To demystify the jargon surrounding machine learning in a clinical context.

Main Methods:

  • Review of fundamental principles of artificial neural networks.
  • Explanation of common machine learning algorithm types.
  • Guidance on applying AI insights in neurological practice.

Main Results:

  • The study provides a foundational understanding of AI in medicine.
  • Clinicians can learn to critically appraise AI-driven clinical insights.
  • Practical applications of AI for neurologists are outlined.

Conclusions:

  • Artificial intelligence and machine learning are integral to future clinical practice.
  • Neurologists need to understand AI to leverage its full potential.
  • This work serves as a primer for integrating AI into neurological care.