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

Stephen D Auger¹, Benjamin M Jacobs^2,3, Ruth Dobson^2,3

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

Practical Neurology

|September 30, 2020

Summary

This summary is machine-generated.

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:

Neuroradiology clinical neurology evidence-based neurology health policy & practice image 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.