AI Used to Predict Alzheimer’s Disease

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that leads to cognitive decline and memory loss, severely affecting millions worldwide. Early detection and accurate prediction of Alzheimer's are critical for timely interventions. This paper explores the application of Artificial Intelligence (AI) in predicting Alzheimer's disease, focusing on machine learning (ML) models, neural networks, and deep learning (DL) techniques. By analyzing a combination of neuroimaging data, genetic information, and cognitive test results, AI systems can identify subtle patterns and biomarkers that indicate the onset of AD even before the appearance of clinical symptoms. The paper discusses the integration of AI with brain imaging technologies, such as MRI and PET scans, as well as the role of natural language processing (NLP) in evaluating speech and text patterns. Key challenges such as data quality, interpretability, and the need for large, diverse datasets are also addressed. The potential for AI to enhance diagnostic accuracy and facilitate personalized treatment approaches in Alzheimer’s care is highlighted, along with future directions for research in this field. The results suggest that AI has the capacity to significantly improve early detection and intervention strategies, ultimately advancing the fight against Alzheimer's disease.