Ethical and Privacy-Preserving AI for Predicting Cognitive Decline in the Elderly Through Long-Term Health Monitoring: A Survey

This record provides the full paper “Ethical and Privacy-Preserving AI for Predicting Cognitive Decline in the Elderly Through Long-Term Health Monitoring: A Survey.” We review 20 studies (2019–2025) and map the interconnections between data sources (wearables, smart homes, EHRs, HRS, robots), AI pipelines, and ethical safeguards in a unified architecture, highlighting trade-offs across privacy, autonomy, fairness, and trust. No single study covers the full ethical spectrum. Key findings: Transparency & Trust is most addressed, while Equity/Accessibility and Compassion are least covered; ML/DL dominate, whereas privacy-preserving methods (FL/DP/SMPC) and practical XAI are rare; about 75% of works are reviews rather than real-world deployments. We conclude with actionable guidance: participatory co-design, layered user-defined privacy, age-inclusive datasets with fairness audits, and explainable, human-centered interfaces.