AI-Aided Diagnosis For Neurodegenerative Diseases: Prospects And Challenges

Chuanzhe Chen

doi:10.62051/72thpj80

Authors

Chuanzhe Chen

DOI:

https://doi.org/10.62051/72thpj80

Keywords:

artificial intelligence; neurodegenerative diseases; diagnosis; biomarkers; machine learning; deep learning.

Abstract

Neurodegenerative disease (ND) represents a chronic disease characterized by loss of neuron function and death, such as Alzheimer's disease, Parkinson's disease, etc. It’s difficult and complex to diagnose ND, due to the requirement for the synthesis of multiple biomarker data, such as genes, proteins, images, etc. The advent of artificial intelligence (AI) technology, particularly through machine learning (ML) and deep learning (DL) algorithms, introduces novel methodologies and tools for ND diagnosis. These methodologies extract pertinent information and patterns from massive, multidimensional, and non-linear data, assisting doctors to render more precise, prompt, and objective assessments. This article provides an overview of the updated implementation status of AI in ND diagnosis, assesses the advantages and contributions of AI to ND diagnosis, as well as the existing limitations and challenges, and offers insights to the future development direction of AI in ND diagnosis.

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