Dipali Prakash Patil,
- Assistant Professor, Department of Master Computer Applications, STES Sinhgad Institute of Management, Vadgaon (Bk), Pune, Maharashtra, India
Abstract
Population aging is accelerating worldwide, and with it the burden of cognitive health conditions such as mild cognitive impairment (MCI), Alzheimer’s disease (AD), and dementia. Detecting and monitoring cognitive change early is central to timely intervention, yet conventional diagnostic tools often miss the subtle signals that appear before overt symptoms. Artificial intelligence (AI) has emerged as a promising complement to clinical assessment because it can work through high-dimensional data and surface patterns that standard analysis tends to overlook. This review synthesizes recent research on AI methods – machine learning (ML), deep learning (DL), hybrid architectures, and reinforcement learning – applied to cognitive-deviation tracking in older adults across neuroimaging, electrophysiology, speech and language, wearable-sensor, and cognitive-assessment data. Following PRISMA 2020 guidance, we organize the evidence thematically around model families, data modalities, and application focus. Convolutional neural networks and transformer-based architectures show consistently strong performance, particularly in multimodal settings, though reported accuracy varies widely with dataset composition and staging definitions. Persistent limitations include a lack of demographic diversity in training data, weak sensitivity to preclinical change, opacity of many models, and unresolved questions of privacy, consent, and explainability. We conclude that translating these methods into practice will depend on inclusive datasets, improved early-detection methods, explainable AI, and privacy-preserving data-sharing infrastructure such as federated learning. The review is intended as a practical reference for researchers, clinicians, and policymakers working at the intersection of AI and healthy cognitive aging.
Keywords: Cognitive deviation, artificial intelligence, aging populations, early detection, explainable AI, deep learning, dementia
[This article belongs to Research and Reviews: A Journal of Neuroscience ]
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Research and Reviews: A Journal of Neuroscience
| Volume | 16 | |
| Issue | 02 | |
| Received | 13/05/2026 | |
| Accepted | 08/06/2026 | |
| Published | 31/08/2026 | |
| Publication Time | 110 Days |