Artificial General Intelligence for the Detection of Neurodegenerative Disorders

Area of Science:

• Neuroscience and Artificial Intelligence
• Computational Medicine
• Gerontology

Background:

• Parkinson's disease and Alzheimer's disease are prevalent age-related neurodegenerative disorders.
• Current diagnostic tools include pathological, genetic, radiological, and clinical examinations.
• Aging demographics in developed countries increase the prevalence of these diseases.

Purpose of the Study:

• To understand neurodegenerative disorders, artificial general intelligence (AGI), and AGI's role in disease detection and prediction.
• To discuss the efficacy of AGI in analyzing diagnostic data for neurodegenerative disease detection.
• To present an Internet of Things (IoT)-based framework for ubiquitous monitoring and treatment.

Main Methods:

• Review and synthesis of current knowledge on neurodegenerative diseases and AGI.
• Analysis of diagnostic data using AGI for disease detection and prediction.
• Conceptualization of an IoT-based framework for patient monitoring and treatment.

Main Results:

• Demonstrates the potential of AGI, including large language models, in processing vast datasets for neurodegenerative disease insights.
• Highlights the application of AGI in analyzing complex diagnostic information for improved detection and prediction.
• Proposes a novel IoT framework for continuous health monitoring and intervention.

Conclusions:

• AGI offers a powerful approach to enhance the early detection and prediction of Parkinson's and Alzheimer's diseases.
• The proposed IoT framework provides a foundation for integrated, real-time patient care.
• Identifies future research directions to address current challenges in AGI-driven neurodegenerative disease management.