This review evaluates flexible bioelectronic sensors combined with AI for continuous remote monitoring and closed-loop interventions in Parkinson's disease, highlighting material advances, multimodal fusion architectures, edge AI for privacy, and a translational gap due to small cohort validation…

While not identifying molecular therapeutic targets, the work is moderately valuable for Parkinson's drug discovery because robust, validated digital biomarkers and closed-loop monitoring can improve clinical endpoint sensitivity, enable remote trial assessments, and accelerate evaluation of…

Effective Parkinson's disease (PD) management is hindered by the intermittent nature of clinical snapshots and the discomfort of rigid monitoring hardware. This review critically evaluates the synergy between flexible bioelectronics and artificial intelligence (AI) for continuous remote monitoring. Our analysis reveals that while material innovations have achieved milligram-level sensitivity, a significant 'translational gap' persists due to limited validation in real-world environments and small cohort sizes. We conclude that multimodal fusion architectures are essential for accurately mapping digital biomarkers to clinical gold standards such as MDS-UPDRS. By leveraging edge AI for privacy and closed-loop feedback for intervention, this integration facilitates the transition from reactive clinical visits to proactive, personalized digital home-care ecosystems.