Remote patient monitoring using artificial intelligence: Current state, applications, and challenges
Article
Article Title | Remote patient monitoring using artificial intelligence: Current state, applications, and challenges |
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ERA Journal ID | 201715 |
Article Category | Article |
Authors | Shaik, Thanveer, Tao, Xiaohui, Higgins, Niall, Li, Li, Gururajan, Raj, Zhou, Xujuan and Acharya, U. Rajendra |
Journal Title | WIREs Data Mining and Knowledge Discovery |
Journal Citation | 13 (2) |
Article Number | e1485 |
Number of Pages | 31 |
Year | 2023 |
Publisher | John Wiley & Sons |
Place of Publication | United Kingdom |
ISSN | 1942-4787 |
1942-4795 | |
Digital Object Identifier (DOI) | https://doi.org/10.1002/widm.1485 |
Web Address (URL) | https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.1485 |
Abstract | The 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. |
Keywords | artificial intelligence; IoT; noninvasive technology; remote patient monitoring |
Related Output | |
Is part of | Revolutionizing healthcare with federated reinforcement learning: from machine learning to machine unlearning |
Article Publishing Charge (APC) Funding | School/Centre |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460201. Artificial life and complex adaptive systems |
Public Notes | This article is part of a UniSQ Thesis by publication. See Related Output. |
Byline Affiliations | School of Mathematics, Physics and Computing |
Department of Health, Queensland | |
Queensland University of Technology | |
Wuhan University of Technology, China | |
School of Business | |
Singapore University of Social Sciences (SUSS), Singapore |
https://research.usq.edu.au/item/v860z/remote-patient-monitoring-using-artificial-intelligence-current-state-applications-and-challenges
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WIREs Data Min Knowl - 2023 - Shaik - Remote patient monitoring using artificial intelligence Current state .pdf | ||
License: CC BY-NC-ND 4.0 | ||
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