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@article{Zhang:2023:10.1016/j.bios.2022.114825,
author = {Zhang, Y and Hu, Y and Jiang, N and Yetisen, AK},
doi = {10.1016/j.bios.2022.114825},
journal = {Biosensors and Bioelectronics},
title = {Wearable artificial intelligence biosensor networks},
url = {http://dx.doi.org/10.1016/j.bios.2022.114825},
volume = {219},
year = {2023}
}
TY - JOUR
AB - The demand for high-quality healthcare and well-being services is remarkably increasing due to the ageing global population and modern lifestyles. Recently, the integration of wearables and artificial intelligence (AI) has attracted extensive academic and technological attention for its powerful high-dimensional data processing of wearable biosensing networks. This work reviews the recent developments in AI-assisted wearable biosensing devices in disease diagnostics and fatigue monitoring demonstrating the trend towards personalised medicine with highly efficient, cost-effective, and accurate point-of-care diagnosis by finding hidden patterns in biosensing data and detecting abnormalities. The reliability of adaptive learning and synthetic data and data privacy still need further investigation to realise personalised medicine in the next decade. Due to the worldwide popularity of smartphones, they have been utilised for sensor readout, wireless data transfer, data processing and storage, result display, and cloud server communication leading to the development of smartphone-based biosensing systems. The recent advances have demonstrated a promising future for the healthcare system because of the increasing data processing power, transfer efficiency and storage capacity and diversifying functionalities.
AU - Zhang,Y
AU - Hu,Y
AU - Jiang,N
AU - Yetisen,AK
DO - 10.1016/j.bios.2022.114825
PY - 2023///
SN - 0956-5663
TI - Wearable artificial intelligence biosensor networks
T2 - Biosensors and Bioelectronics
UR - http://dx.doi.org/10.1016/j.bios.2022.114825
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000879531300004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
VL - 219
ER -