Artificial Intelligence and Digital Biomarkers for Early Detection and Monitoring of Neurological Disorders: A Narrative Review
Abstract
Neurological disorders like Alzheimer's disease, Parkinson's disease, and epilepsy are becoming major causes of disability and mortality worldwide, and their prevalence is expected to rapidly increase with the aging of the population. These diseases develop silently, with irreversible neuronal damage often occurring decades before any clinical signs of illness are noticed, making early diagnosis and treatment difficult. The presymptomatic period greatly restricts the effectiveness of therapeutic interventions and reduces the possibility of disease-modifying interventions. Traditional diagnostic methods based on clinical assessment, neuroimaging, and invasive biomarkers are not sensitive enough to identify the disease at an early stage and are expensive to the healthcare system. The latest artificial intelligence (AI) technology and machine learning (ML) approaches, together with digital biomarkers obtained from eye tracking, facial expressions, speech analysis, motor dynamics, electrophysiology, wearable devices, and passive sensing, offer promising non-invasive alternatives for early detection of diseases. However, most reported performance metrics are derived from retrospective or pre-validated datasets, and prospective external validation remains limited. This narrative review synthesizes current evidence on AI-driven digital biomarkers for early detection of neurological diseases, examining disease-specific applications, methodological approaches, and challenges in…
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