Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Thanveer Shaik; Xiaohui Tao; Niall Higgins; Lin Li; Raj Gururajan; Xujuan Zhou; U. Rajendra Acharya

doi:10.1002/widm.1485

journal article Open Access Jan 05, 2023

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Thanveer Shaik

U. Rajendra Acharya

WIREs Data Mining and Knowledge Discovery Vol. 13 No. 2 · Wiley

View at Publisher Save 10.1002/widm.1485

Abstract

AbstractThe adoption of artificial intelligence (AI) in healthcare is growing rapidly. Remote patient monitoring (RPM) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in‐home care, and even hospitalized patients. The reliability of manual patient monitoring systems depends on staff time management which is dependent on their workload. Conventional patient monitoring involves invasive approaches which require skin contact to monitor health status. This study aims to do a comprehensive review of RPM systems including adopted advanced technologies, AI impact on RPM, challenges and trends in AI‐enabled RPM. This review explores the benefits and challenges of patient‐centric RPM architectures enabled with Internet of Things wearable devices and sensors using the cloud, fog, edge, and blockchain technologies. The role of AI in RPM ranges from physical activity classification to chronic disease monitoring and vital signs monitoring in emergency settings. This review results show that AI‐enabled RPM architectures have transformed healthcare monitoring applications because of their ability to detect early deterioration in patients' health, personalize individual patient health parameter monitoring using federated learning, and learn human behavior patterns using techniques such as reinforcement learning. This review discusses the challenges and trends to adopt AI to RPM systems and implementation issues. The future directions of AI in RPM applications are analyzed based on the challenges and trends.This article is categorized under:
Application Areas > Health Care
Technologies > Artificial Intelligence
Technologies > Internet of Things

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References

166

[1]

10.4018/ijehmc.20210901.oa5

[2]

10.1093/jamia/ocw048

[3]

10.1016/j.compbiomed.2017.08.015

[4]

10.1007/s11886-021

[5]

10.1001/jamaneurol.2021.0023

[6]

10.2196/23498

[7]

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Moloud Abdar, Farhad Pourpanah, Sadiq Hussain et al.

Information Fusion 10.1016/j.inffus.2021.05.008

[8]

10.1027/0227-5910/a000561

[9]

10.32604/cmc.2021.016938

[10]

10.1109/iceeot.2016.7755040

[11]

10.1109/access.2020.3047186

[12]

10.3390/s21248226

[13]

10.3390/s21113845

[14]

10.1016/j.gastre.2017.03.012

[15]

10.1016/j.jemermed.2019.11.020

[16]

10.1016/j.ijmedinf.2017.10.002

[17]

10.1038/s42256-018-0004-1

[18]

10.1109/ccssp49278.2020.9151713

[19]

10.1016/j.arth.2018.02.067

[20]

10.1177/2472630319890316

[21]

10.3390/app9204364

[22]

10.1002/hec.4523

[23]

10.1021/acs.chemrev.1c00502

[24]

10.1146/annurev-biodatasci-092820-033938

[25]

Chen K. "Deep learning for sensor‐based human activity recognition" ACM Computing Surveys (2021) 10.1145/3447744

[26]

10.1109/mis.2020.2988604

[27]

10.1109/acii.2017.8273639

[28]

10.1016/j.eswa.2020.114041

[29]

10.1007/s00521-021-06012-8

[30]

10.1109/jbhi.2017.2751509

[31]

10.1136/bmjinnov-2021-000668

[32]

10.1016/j.eswa.2020.113905

[33]

10.1164/rccm.202109-2092oc

[34]

10.1007/s11227-019-02873-y

[35]

10.3390/s18082414

[36]

10.1016/j.ijnurstu.2018.04.013

[37]

10.7326/m19-0283

[38]

10.1016/j.apergo.2020.103149

[39]

10.1007/978-3-030-52856-0_47

[40]

10.3390/e21030305

[41]

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Nora El-Rashidy, Shaker El-Sappagh, S. Islam et al.

Diagnostics 10.3390/diagnostics11040607

[42]

10.5811/westjem.2020.5.48165

[43]

10.1109/icdh52753.2021.00054

[44]

10.1016/j.ress.2021.108019

[45]

10.1016/j.bpa.2020.12.013

[46]

10.1016/j.inffus.2021.04.017

[47]

Gawlikowski J. Tassi C. R. N. Ali M. Lee J. Humt M. Feng J. Kruspe A. Triebel R. Jung P. Roscher R. &Shahzad M.(2021).A survey of uncertainty in deep neural networks.https://doi.org/10.48550/ARXIV.2107.03342

[48]

10.1016/j.artmed.2021.102062

[49]

Hambali A. M. "Ovarian cancer classification using hybrid synthetic minority over‐sampling technique and neural network" Journal of Advances in Computer Research (2016)

[50]

10.1109/sieds.2018.8374719

Showing 50 of 166 references

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Metrics

360

Citations

166

References

Details

Published: Jan 05, 2023
Vol/Issue: 13(2)
License: View

Authors

T

Thanveer Shaik

School of Mathematics, Physics and Computing University of Southern Queensland Toowoomba Australia

X

Xiaohui Tao

School of Mathematics, Physics and Computing University of Southern Queensland Toowoomba Australia

N

Niall Higgins

Metro North Hospital and Health Service Royal Brisbane and Women's Hospital Brisbane Australia; School of Nursing Queensland University of Technology Brisbane Australia

L

Lin Li

School of Computer and Artificial Intelligence Wuhan University of Technology Wuhan China

R

Raj Gururajan

School of Business University of Southern Queensland Springfield Australia

X

Xujuan Zhou

School of Business University of Southern Queensland Springfield Australia

U

U. Rajendra Acharya

School of Science and Technology Singapore University of Social Sciences Singapore

Cite This Article

Thanveer Shaik, Xiaohui Tao, Niall Higgins, et al. (2023). Remote patient monitoring using artificial intelligence: Current state, applications, and challenges. WIREs Data Mining and Knowledge Discovery, 13(2). https://doi.org/10.1002/widm.1485

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

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