Healthcare Diagnosis and Treatment in the Metaverse: Remote Sensing Algorithms, Networked Wearable Devices, and Virtual Patient Data
Healthcare Diagnosis and Treatment in the Metaverse: Remote Sensing Algorithms, Networked Wearable Devices, and Virtual Patient Data

Summary/Abstract: I draw on a substantial body of theoretical and empirical research on metaverse healthcare systems deploying medical imaging devices, virtual patient data, and physiological and behavioral sensors. With increasing evidence of digital twin simulation tools, health-related biometric and genomic data, and Internet of Things-based smart healthcare systems enabling metaverse operations management as regards disease diagnosis and treatment through remote monitoring devices, there is an essential demand for comprehending whether 3D body scanning technologies, deep generative algorithms, and telemedicine services are instrumental in cloud-based personalized healthcare systems. In this research, prior findings were cumulated indicating that virtual simulation systems leverage healthcare modeling and simulation tools in the virtual medical environment. I carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout May 2022, with search terms including “healthcare diagnosis and treatment in the metaverse” + “remote sensing algorithms,” “networked wearable devices,” and “virtual patient data.” As I analyzed research published in 2022, only 114 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, I decided on 30, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, ROBIS, and SRDR.

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