Applications of Artificial Intelligence to Diagnosis of Neurodegenerative Diseases.

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Abstract

Artificial Intelligence (AI) is an umbrella term that represents a new technology for simulating and expanding human intelligence by using machines and computer systems. It consists of methods such as machine learning (ML), deep learning (DL), and natural language processing (NLP). In the era of big data, AI has emerged as an essential tool for improving the detection of neurodegenerative diseases, such as Alzheimer’s diseases (AD), Parkinson’s diseases, amyotrophic lateral sclerosis, etc. AI with its ability to extract critical information from the mass of data has enabled scientists to deal with various types of large-volume data, including genetic data, imaging data, and clinical data, rapidly generated in the course of neurodegenerative disease research. This review provides a comprehensive overview of the literature on current AI applications in the diagnosis of neurodegenerative diseases. Firstly, bioinformatics and AI approaches to identify potential biomarkers for neurodegenerative diseases such as AD are reviewed. Secondly, the use of ML and DL methods to analyze Magnetic Resonance Imaging (MRI) data for a better understanding of disease progression and predicting patient outcomes is discussed. Finally, the use of AI methods including NLP for Electronic Health Record (EHR) data analysis to extract meaningful information and identify patterns that may contribute to early diagnosis and treatment planning are reviewed. The potential benefits of AI-based approaches in improving patient outcomes and the challenges associated with their implementations are also discussed. Overall, this paper highlights the promise of AI in transforming the diagnosis and management of neurodegenerative diseases.