Volume 6 • Issue 1 • PP: 72–93 • 2026
Artificial Intelligence-Assisted Alzheimer’s Disease Research: A Review of Pathology, Early Diagnosis, Biomarkers, Therapeutic Challenges, and Care Implications
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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.
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