Digital Twin-based Disease Condition and Deep Learningbased Immersive Healthcare Simulations, Big Healthcare Data Analysis Visualization, and Internet of Things-based Smart Health Monitoring Systems for Artificial Intelligence-based Remote Patient...
Digital Twin-based Disease Condition and Deep Learningbased Immersive Healthcare Simulations, Big Healthcare Data Analysis Visualization, and Internet of Things-based Smart Health Monitoring Systems for Artificial Intelligence-based Remote Patient...
Author(s): George LăzăroiuSubject(s): Health and medicine and law, Demography and human biology, ICT Information and Communications Technologies
Published by: Addleton Academic Publishers
Keywords: digital twin-based disease condition; deep learning-based immersive healthcare simulation; big healthcare data; Internet of Things-based smart health monitoring; artificial intelligence-based remote patient management;
Summary/Abstract: In this article, previous research findings were cumulated, indicating that extended reality-based metaverse wearables and 3D augmented reality-based medical data visualization can be harnessed in personalized care and treatment, clinical data management, and healthcare big data processing in virtual consultation spaces. Throughout February 2024, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “artificial intelligence-based remote patient management in the interactive virtual healthcare metaverse” + “digital twin-based disease condition and deep learning-based immersive healthcare simulations,” “big healthcare data analysis visualization,” and “Internet of Things-based smart health monitoring systems.” As research published between 2022 and 2024 was inspected, only 170 articles satisfied the eligibility criteria, and 29 mainly empirical sources were selected. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: Abstrackr, Catchii, Eppi-Reviewer, JBI SUMARI, MMAT, and Systematic Review Accelerator.
Journal: American Journal of Medical Research
- Issue Year: 11/2024
- Issue No: 1
- Page Range: 55-70
- Page Count: 16
- Language: English
- Content File-PDF