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Integrating Artificial Intelligence in Alzheimer’s Disease Detection and Classification: A Comprehensive Review

artificial-intelligence
computer-education
computer-vision
data-mining-and-machine-learning

Abstract

Alzheimer's Disease (AD) is a progressive brain disorder and the most common cause of dementia, creating a substantial challenge for healthcare systems worldwide. Detecting it early and accurately is essential for management, but remains difficult due to vague and varied early signs. Artificial Intelligence (AI) and deep learning are emerging as powerful tools that can analyze complex medical data to change how AD is diagnosed. This review thoroughly explores the use of these AI methods for detecting and classifying Alzheimer's Disease. This paper presents a detailed exploration of Artificial Intelligence (AI) and its essential role in the identification and classification of AD. We conduct a comprehensive review of multiple AI paradigms, including Machine Learning, Deep Learning, Explainable AI (XAI), Hybrid models, Graph Neural Networks, Federated Learning, and speech and Natural Language Processing-based detection, analyzing each for its strengths and limitations in delivering detection and classification of AD insights. Ultimately, we underscore the critical applications, challenges, and limitations of AI and future directions, arguing for its responsible and ethical use to foster trust and transparency in AD detection and classification. This review aims to serve as a foundational guide for future studies, thereby enriching the literature on AI in AD classification. By consolidating the current evidence, this review concludes that AI is a powerful tool capable of transforming Alzheimer's diagnostics through marked improvements in accuracy, accessibility, and speed. This review will serve for researchers, engineers, emerging scholars, and stakeholders specializing in the use of artificial intelligence (AI) and machine learning (ML) for Alzheimer's disease (AD) research.
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