Machine learning, artificial intelligence and the prediction of dementia

Area of Science:

• Medical Informatics
• Computational Neuroscience
• Artificial Intelligence in Medicine

Background:

• Artificial intelligence (AI) and machine learning (ML) are increasingly integral to medical advancements.
• AI facilitates automated analytical modeling for disease prediction, diagnostics, and treatment planning.

Purpose of the Study:

• To provide an overview of AI applications specifically within dementia research.
• To explore the role of ML and Deep Learning in dementia diagnosis and prediction.

Main Methods:

• Review of current literature on AI and ML applications in dementia.
• Analysis of Deep Learning versus traditional ML methods for dementia detection and prediction.

Main Results:

• Deep Learning models show potential for higher accuracy in dementia detection and prediction compared to traditional ML.
• Limitations include high computational costs for Deep Learning and scarcity of large datasets for training ML models.
• Current commercial applications are limited, primarily to mobile health tools with basic ML functionalities.

Conclusions:

• AI and ML technologies offer significant advantages for psychiatry and neurology in understanding dementia.
• These technologies can lead to more accurate, reproducible, and standardized evidence-based diagnostic and treatment processes.