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Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.

Mirko Jerber Rodríguez Mallma¹, Luis Zuloaga-Rotta¹, Rubén Borja-Rosales¹

¹Facultad de Ingeniería Industrial y de Sistemas, Universidad Nacional de Ingeniería, Lima 15333, Peru.

Neurology International

|November 25, 2024

Summary

This summary is machine-generated.

This review explores explainability in machine learning (ML) for brain diseases. It highlights the need for transparent AI in healthcare, analyzing 133 studies on ML models and explainability techniques.

Keywords:

brain diseases diagnosis explainable artificial intelligence (XAI)healthcare machine learning (ML)prognosis risk

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Area of Science:

Medical Informatics
Artificial Intelligence
Neuroscience

Background:

Machine learning (ML) models show promise in healthcare, but their clinical application requires transparency.
Explainability is crucial for reliable medical decision-making using AI.

Purpose of the Study:

To systematically review the application and explainability of ML models in brain disease research.
To identify common ML models and explainability techniques used in this domain.

Main Methods:

A systematic literature search was performed across Web of Science, Scopus, and PubMed databases.
Studies published between January 2014 and December 2023 were included.
133 relevant studies were analyzed from an initial pool of 682.

Main Results:

The review identified 11 distinct ML models and 12 explainability techniques applied to 20 different brain diseases.
Explainable AI (XAI) is increasingly important for validating ML in neurological studies.

Conclusions:

Explainability is a key factor for the trustworthy implementation of ML in brain disease diagnosis and research.
Further research can focus on developing and applying advanced explainability methods for complex neurological conditions.