AI and Biomarkers: New Frontiers in Alzheimer’s and Dementia Diagnosis

The landscape of Alzheimer’s disease (AD) diagnosis is shifting toward the integration of non-invasive biomarkers and artificial intelligence to enable earlier detection and more precise staging of the neurodegenerative disorder.

Alzheimer’s disease is characterized by the deposition of β-amyloid (Aβ) plaques and the formation of neurofibrillary tangles consisting of hyperphosphorylated tau protein. These pathological changes lead to neuronal loss and cognitive decline.

Advancements in Non-Invasive Biomarkers

Traditional diagnostic methods, such as clinical evaluations, cerebrospinal fluid biomarker testing, and neuroimaging, often face challenges regarding operational complexity and insufficient sensitivity or specificity.

Recent technological developments have introduced several novel, less invasive methods for identifying the disease:

• Blood-based detection of tau protein and Aβ.
• Ocular biomarker testing.
• Non-invasive screening utilizing breath or urine analysis.
• The use of microfluidic chips and biosensor technologies to provide rapid and cost-effective diagnosis.

These tools are being integrated into multimodal diagnostic frameworks that combine genomics, proteomics, and imaging to improve the accuracy of early diagnosis.

The Role of Artificial Intelligence and Digital Biomarkers

Artificial intelligence (AI) is being applied to the diagnosis, treatment, and prognostic modeling of Alzheimer’s disease to transform dementia care.

A bibliometric analysis of 431 studies and a scoping review of 86 AI models have highlighted the emergence of digital biomarkers. These markers focus on physiological and behavioral characteristics, including:

• Speech analysis, which serves as a proxy for cognitive assessment.
• Eye tracking.
• Neurocognitive tests.
• Motor activity.

Research indicates that classical machine learning models currently dominate the field. Among 21 AI models focused specifically on Alzheimer’s disease, the average Area Under the Curve (AUC) is 0.887. For 45 models targeting mild cognitive impairment, the average AUC is 0.821.

Clinical Implementation and Challenges

Despite the technical progress, integrating digital biomarkers into clinical practice remains challenging. A review of existing AI models found that only three studies performed model calibration and only two incorporated external validation.

The global urgency for these innovations is driven by an aging population. According to the World Health Organization, more than 55 million people live with dementia, with Alzheimer’s disease accounting for 60% to 70% of those cases.

Current research is also exploring the use of integrated algorithms that combine plasma biomarkers with cognitive assessments to accurately predict brain β-amyloid pathology.

The goal of these combined AI-driven and biomarker-based solutions is to move toward a frontier of neurodiagnostic tools that can support the prevention and management of the disease before severe cognitive impairment occurs.