AI-Enabled Flexible Sensing Ecosystems for Parkinson's Disease: Advancing Digital Biomarkers and Closed-Loop Interventions.

Jiadong Jin, Yongchang Jiang, Yukai Zhou, Wenkai Zhu, Jiangbo Hua, Wen Cheng, Yi Shi, Lijia Pan

Abstract readReview

In one paragraph

Review in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jiadong JinCollaborative Innovation Center of Advanced Microstructures, Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China.

Yongchang JiangCollaborative Innovation Center of Advanced Microstructures, Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China.

Yukai ZhouCollaborative Innovation Center of Advanced Microstructures, Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China.

Wenkai ZhuCollaborative Innovation Center of Advanced Microstructures, Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China.

Jiangbo HuaCollaborative Innovation Center of Advanced Microstructures, Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China.

Wen ChengSchool of Integrated Circuits, Nanjing University, Suzhou 215163, China.ORCID 0000-0002-9063-1177

Yi ShiCollaborative Innovation Center of Advanced Microstructures, Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China.

Lijia PanCollaborative Innovation Center of Advanced Microstructures, Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials, School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China.ORCID 0000-0002-8917-7843

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Artificial IntelligenceBiomarkersBiosensing TechniquesParkinson DiseaseDigital HealthEcosystemHumansBiomarkersartificial intelligencedeep learningdigital biomarkersflexible sensorsfreezing of gaitParkinson’s disease

Identifiers

PMID41977856

DOI10.3390/s26072071

PMCPMC13074508

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.