Explainable ai for Alzheimer detection: A review of current methods and applications
Alzheimer’s disease (AD) is the most common cause of dementia, marked by cognitive decline and memory loss. Recently, machine learning and deep learning techniques have introduced promising solutions for improving AD detection through MRI, especially in settings where specialists may not be readily...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
Multidisciplinary Digital Publishing Institute (MDPI)
2024
|
| Online Access: | http://eprints.utem.edu.my/id/eprint/28705/2/02638271220242244111525.pdf http://eprints.utem.edu.my/id/eprint/28705/ https://www.mdpi.com/2076-3417/14/22/10121 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Alzheimer’s disease (AD) is the most common cause of dementia, marked by cognitive decline and memory loss. Recently, machine learning and deep learning techniques have introduced promising solutions for improving AD detection through MRI, especially in settings where specialists may not be readily available. These techniques offer the potential to assist general practitioners and non-specialists in busy clinical environments. However, the ‘black box’ nature of many AI techniques makes it challenging for non-expert physicians to fully trust their diagnostic accuracy. In this review, we critically evaluate current explainable AI (XAI) methods applied to AD detection and highlight their limitations. In addition, a new interpretability framework, called “Feature-Augmented”, was theoretically designed to improve model interpretability. This approach remains underexplored, primarily due to the scarcity of explainable AD-specific datasets. Furthermore, we underscore the importance of AI models being accurate and explainable, which enhance diagnostic confidence and patient care outcomes. |
|---|