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


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.
Ask to review this manuscript

Notes for potential reviewers

  • Volunteering is not a guarantee that you will be asked to review. There are many reasons: reviewers must be qualified, there should be no conflicts of interest, a minimum of two reviewers have already accepted an invitation, etc.
  • This is NOT OPEN peer review. The review is single-blind, and all recommendations are sent privately to the Academic Editor handling the manuscript. All reviews are published and reviewers can choose to sign their reviews.
  • What happens after volunteering? It may be a few days before you receive an invitation to review with further instructions. You will need to accept the invitation to then become an official referee for the manuscript. If you do not receive an invitation it is for one of many possible reasons as noted above.

  • PeerJ Computer Science does not judge submissions based on subjective measures such as novelty, impact or degree of advance. Effectively, reviewers are asked to comment on whether or not the submission is scientifically and technically sound and therefore deserves to join the scientific literature. Our Peer Review criteria can be found on the "Editorial Criteria" page - reviewers are specifically asked to comment on 3 broad areas: "Basic Reporting", "Experimental Design" and "Validity of the Findings".
  • Reviewers are expected to comment in a timely, professional, and constructive manner.
  • Until the article is published, reviewers must regard all information relating to the submission as strictly confidential.
  • When submitting a review, reviewers are given the option to "sign" their review (i.e. to associate their name with their comments). Otherwise, all review comments remain anonymous.
  • All reviews of published articles are published. This includes manuscript files, peer review comments, author rebuttals and revised materials.
  • Each time a decision is made by the Academic Editor, each reviewer will receive a copy of the Decision Letter (which will include the comments of all reviewers).

If you have any questions about submitting your review, please email us at [email protected].