ReviewRSC advances2025
Applications of flexible materials in health management assisted by machine learning.
Review in RSC advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, the demand for improved health management has become increasingly higher; however, the existing medical resources have made it difficult to meet this demand. The field of health management is in urgent need for self-help monitoring equipment, intelligent identification technology and personalized medical services. This article reviews the application of flexible materials in health management, particularly the application of flexible wearable sensing devices combined with machine learning technology in various medical scenarios, and classifies them into several types of applications such as health monitoring and prevention, disease diagnosis and treatment, rehabilitation treatment and assistance. Flexible materials can be used to fabricate or integrate various types of high-sensitivity sensors with the characteristics of high flexibility and self-adhesion, resulting in a wealth of health monitoring equipment. These devices can self-monitor various physiological indicators in various parts of the human body. The integration of machine learning (ML) makes it possible to analyze and identify subtle, massive, multi-channel and multi-modal sensor data, accelerating the intelligent process of health management and personalized medicine. This paper not only elaborates on various flexible materials and ML algorithms commonly used in the field of health management, but also focuses on discussing the application of ML-assisted flexible materials in different stages of health management, and puts forward prospects for the future development direction, providing reference and inspiration for major changes in the field of health management.
Identifiers
What OpenQuestion holds
Registered trials
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.