Next-Generation Brain Health Monitoring Enabled by Integrated Wearable AI Infrastructure

Neurodegenerative diseases are difficult to detect during their pre-symptomatic stages, when intervention is most effective. Advances in wearable biosensors, artificial intelligence (AI), and neuroscience now enable continuous monitoring of brain-related physiological signals in real-world settings. However, progress in sensing technologies, signal analytics, and machine learning remains fragmented, limiting the biological specificity, robustness, and clinical translation of many digital biomarkers. This review presents an integrated framework that links molecular pathology and neural circuit dysfunction to sensor-detectable digital phenotypes across sleep, autonomic regulation, gait, speech, and ocular dynamics. We evaluate emerging wearable platforms, including bioelectric, optical, and neuromotor sensors, and discuss critical design considerations such as signal quality, user-centered form factors, and ultra-low-power edge AI systems. We further examine how self-supervised learning, federated AI, mechanistic modeling, and fairness-aware validation can improve biomarker reliability, interpretability, and generalizability. To facilitate clinical deployment, we introduce the Personalized Brain-Health Operating System (pBH-OS), a privacy-preserving reference architecture for scalable digital phenotyping. The framework maps conceptual layers to existing technologies while identifying key validation gaps. We also address practical challenges, including adherence, device dropout, informative missingness, regulatory compliance, and accountability. Together, these advances provide a pathway toward biologically grounded and regulation-ready systems capable of identifying neurodegenerative risk years before clinical symptom onset.