Artificial Intelligence-Assisted Alzheimer’s Disease Research: A

Review of Pathology, Early Diagnosis, Biomarkers, Therapeutic

Challenges, and Care Implications

Ziad Shendy1,*

1 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt

Received: December 27, 2025 Revised: February 18, 2026 Accepted: April 19, 2026 ⋆ Corresponding author

ABSTRACT

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and one of the major causes of cognitive

decline, functional impairment, and long-term dependency in older adults. Although AD is often associated with

memory loss, its clinical impact extends to language, executive function, attention, behavior, daily living ability,

caregiver burden, and healthcare-system demand. This review examines AD as a multifactorial and clinically

heterogeneous disorder shaped by interacting pathological, molecular, diagnostic, therapeutic, caregiving, and publichealth

dimensions. In addition, the review highlights the growing role of artificial intelligence (AI) in AD research

and clinical support. AI-based approaches are increasingly being explored for neuroimaging analysis, biomarker

interpretation, cognitive assessment, disease-risk prediction, patient stratification, early detection, and longitudinal

monitoring. These methods may support more accurate and timely diagnosis, especially when combined with

clinical evaluation, biomarker evidence, and patient history. However, AI should not be considered a replacement

for clinical judgment. Its value depends on validation, interpretability, ethical use, data quality, accessibility, and

real-world clinical integration. The reviewed literature shows that amyloid beta accumulation, tau pathology, synaptic

dysfunction, neuronal loss, neuroinflammation, oxidative stress, vascular contribution, mixed pathology, and brain

atrophy all contribute to AD progression and clinical variability. Despite advances in biological understanding,

biomarker-based diagnosis, and computational tools, important challenges remain, including subtle early symptoms,

overlap with normal aging and other disorders, unequal access to advanced diagnostics, limited clinical deployment

of AI models, uncertain translation of biological treatment effects into meaningful functional benefit, and substantial

caregiver burden. Overall, this review emphasizes the need for an integrated and patient-centered framework that

connects AD pathology, AI-assisted diagnosis, biomarker development, therapeutic innovation, caregiver support,

and practical healthcare implementation.

Keywords: Artificial intelligence; Alzheimer’s disease; Biomarkers; Early diagnosis; Neurodegeneration

1. INTRODUCTION

Alzheimer’s disease (AD) is a progressive brain disorder and

one of the leading causes of cognitive decline in older adults.

It is often described through memory loss, but its clinical

impact is wider than memory impairment alone. The disease

gradually affects language, attention, reasoning, orientation,

behavior, judgment, and the ability to perform daily activities.

These changes reduce independence and make routine tasks

increasingly difficult, especially as the patient moves from