A new arXiv paper reviews technological advancements in detecting and managing cognitive impairment in older adults, focusing on AI and machine learning applications. The paper synthesizes findings from neurophysiological signals, neuroimaging, blood biomarkers, and digital tools, proposing an integrated early-detection framework. While deep learning models like CNNs and transformers show promise, the authors highlight challenges in standardization, explainability, and external validation for widespread deployment. AI
IMPACT AI and ML advancements are improving early detection and management of cognitive decline, potentially leading to better patient outcomes and personalized care.
RANK_REASON The item is a research paper published on arXiv detailing technological advances in a specific field. [lever_c_demoted from research: ic=1 ai=1.0]
- Alzheimer's disease
- BiLSTM
- CNNs
- deep learning
- donanemab
- electroencephalography
- lecanemab
- LSTM
- machine learning
- magnetic resonance imaging
- p-tau217
- transformers
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
