An Overview of Explainable AI Methods for Diagnosing Brain Diseases
Document Type Article
Array Volume 22, July 2024, 100345
doi:10.1016/j.array.2024.100345
Abstract
In recent years, there has been a significant increase in the
use of AI models in healthcare. These models have been demonstrated to
produce high accuracy in disease diagnosis and classification; however,
they do not reveal the reasoning behind their predictions. Their blackbox
nature makes them untrustworthy for medical diagnosis. However,
eXplainable Artificial Intelligence (XAI) techniques help determine the
basis on which AI models make predictions. This review paper provides an
overview of research conducted in the field of XAI for diagnosing, detecting,
and classifying brain diseases such as brain tumours, Alzheimer’s disease,
Dementia, Parkinson’s disease, Stroke, Epilepsy and Autism Spectrum
Disorder (ASD). It also highlights the importance of XAI techniques and
the significance of the research being conducted in this field. Finally, we
discuss the limitations of current XAI techniques and future research
directions. This study can help doctors, researchers, and policymakers
interested in the interpretability and explainability of AI models in
diagnosing brain diseases.