New study: AI-based EEG and blood biomarkers boost early dementia detection

As populations age, spotting mild cognitive impairment (MCI) before it progresses to dementia is urgent—yet routine clinical checks routinely miss early signs. A new arXiv review (2607.28687) by Mohammad Asif and colleagues synthesizes how neurophysiological signals (EEG), structural/molecular imaging (MRI, amyloid/tau PET), plasma biomarkers, and digital tools are being fused with artificial intelligence to detect cognitive decline earlier. Highlighted advances include EEG markers like alpha/theta power shifts and P300 latency, alongside deep learning architectures (CNNs, LSTM/BiLSTM, transformers, and self-supervised EEG foundation models) that report strong accuracy.

However, the authors caution that most models are trained on small, single-site datasets, making them unlikely to survive rigorous external validation. Despite this, tangible clinical wins exist: plasma p-tau217 has reached utility—the first blood test authorized to aid Alzheimer's diagnosis arrived in 2025—and anti-amyloid drugs lecanemab and donanemab are approved, albeit with modest and debated benefits. Wearables, remote monitoring, speech analysis, and VR tools enable continuous, ecologically valid tracking, and multimodal fusion improves sensitivity. Major obstacles remain: standardization, explainability, data privacy, and equitable deployment. The paper contributes a cross-disciplinary taxonomy, a validation framework, and an integrative early-detection pathway linking tiered screening to intervention—pointing toward trustworthy, longitudinally validated multimodal systems.