Alzheimer’s disease (AD) is the leading cause of dementia, with early diagnosis playing a pivotal role in improving patient outcomes and health care efficiency. This systematic review evaluates current and emerging diagnostic methods from cerebrospinal fluid (CSF) biomarkers and advanced imaging to artificial intelligence (AI) and speech analysis, focusing on their efficacy in detecting mild cognitive impairment and early-stage AD. We synthesize evidence from 127 studies (2015–2025), demonstrating that plasma p-tau217 and electroencephalography offer scalable, low-cost alternatives to positron emission tomography imaging, with comparable accuracy (sensitivity: 88–94%). Socioeconomic analyses reveal that early diagnosis reduces long-term care costs by £7,750 per patient and enables timely interventions to preserve quality of life. However, structural racism, clinician biases, and disparities in resource allocation delay detection in marginalized populations. We propose actionable policy reforms, including subsidized biomarker testing, AI-driven telehealth tools for underserved regions, and antistigma campaigns to promote equitable access. By integrating emerging technologies into primary care and addressing systemic barriers, this review outlines a transformative roadmap for global AD management.
Kimia Kazemi (2025) studied this question.