Evidence mapPaperPMID 40167502Full record

ReviewAdvanced materials (Deerfield Beach, Fla.)2025

Transforming Healthcare: Intelligent Wearable Sensors Empowered by Smart Materials and Artificial Intelligence.

Shuwen Chen, Shicheng Fan, Zheng Qiao, Zixiong Wu, Baobao Lin, Zhijie Li, Michael A Riegler, Matthew Yu Heng Wong, Arve Opheim, Olga Korostynska and 5 more

Abstract readReview
In one paragraph

Review in Advanced materials (Deerfield Beach, Fla.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing 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

34 citing papers in PubMed.

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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

15 authors.

Shuwen ChenInstitute of Medical Equipment Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.ORCID https://orcid.org/0000-0002-6611-3599
Shicheng FanDepartment of Biomedical Engineering, National University of Singapore, Singapore, 117583, Singapore.
Zheng QiaoDepartment of Biomedical Engineering, National University of Singapore, Singapore, 117583, Singapore.
Zixiong WuDepartment of Biomedical Engineering, National University of Singapore, Singapore, 117583, Singapore.
Baobao LinDepartment of Biomedical Engineering, National University of Singapore, Singapore, 117583, Singapore.
Zhijie LiDepartment of Biomedical Engineering, National University of Singapore, Singapore, 117583, Singapore.
Michael A RieglerSimula Metropolitan Center for Digital Engineering, Oslo, 0167, Norway.
Matthew Yu Heng WongSchool of Clinical Medicine, University of Cambridge, Cambridge, CB2 1TN, UK.
Arve OpheimSunnaas Rehabilitation Hospital, Bjoernemyr, 1453, Norway.
Olga KorostynskaDepartment of Mechanical, Electronic and Chemical Engineering (MEK), Faculty of Technology, Art, and Design, TKD, Oslo Metropolitan University, OsloMet, Oslo, 0166, Norway.
Kaare Magne NielsenDepartment of Life Science and Health, Faculty of Health Sciences, Oslo Metropolitan University, OsloMet, Oslo, 0130, Norway.
Thomas GlottSunnaas Rehabilitation Hospital, Bjoernemyr, 1453, Norway.
Anne Catrine T MartinsenSunnaas Rehabilitation Hospital, Bjoernemyr, 1453, Norway.
Vibeke H Telle-HansenIntelligent Health, Faculty of Health Sciences and Faculty of Technology, Art and Design, Oslo Metropolitan University, OsloMet, Oslo, 0130, Norway.ORCID https://orcid.org/0000-0003-0874-1420
Chwee Teck LimDepartment of Biomedical Engineering, National University of Singapore, Singapore, 117583, Singapore.ORCID https://orcid.org/0000-0003-4019-9782

Funding

Advanced Research and Technology Innovation Centre, College of Design and Engineering, National University of Singapore A-0005947-22-00College of Design and Engineering, National University of Singapore A-000936304-00Institute for Health Innovation and Technology, National University of Singapore A-0001415-06-00
6 · The paper itself

Abstract

Intelligent wearable sensors, empowered by machine learning and innovative smart materials, enable rapid, accurate disease diagnosis, personalized therapy, and continuous health monitoring without disrupting daily life. This integration facilitates a shift from traditional, hospital-centered healthcare to a more decentralized, patient-centric model, where wearable sensors can collect real-time physiological data, provide deep analysis of these data streams, and generate actionable insights for point-of-care precise diagnostics and personalized therapy. Despite rapid advancements in smart materials, machine learning, and wearable sensing technologies, there is a lack of comprehensive reviews that systematically examine the intersection of these fields. This review addresses this gap, providing a critical analysis of wearable sensing technologies empowered by smart advanced materials and artificial Intelligence. The state-of-the-art smart materials-including self-healing, metamaterials, and responsive materials-that enhance sensor functionality are first examined. Advanced machine learning methodologies integrated into wearable devices are discussed, and their role in biomedical applications is highlighted. The combined impact of wearable sensors, empowered by smart materials and machine learning, and their applications in intelligent diagnostics and therapeutics are also examined. Finally, existing challenges, including technical and compliance issues, information security concerns, and regulatory considerations are addressed, and future directions for advancing intelligent healthcare are proposed.

Indexed as

Artificial IntelligenceDelivery of Health CareSmart MaterialsWearable Electronic DevicesHumansMachine LearningMonitoring, PhysiologicSmart Materialshealth monitoringintelligent healthcaremachine learningmetamaterialswearable sensors

Identifiers

PMID40167502
PMCPMC12107229

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.