ABSTRACT. The present study systematically reviews the existing research on Internet of Things-based personalized healthcare systems leveraging machine and deep learning algorithms, virtual health assistants and chatbots, and wireless body area networks on Web 3.0 metaverse platforms. My findings clarify that wearable Internet of Medical Things sensor devices, remote patient monitoring systems, and computer vision technologies assist digital twins in healthcare, and I contribute to the literature by indicating that Internet of Things-based healthcare applications, medical big data, and digital twin simulation and modeling tools articulate remote patient monitoring in immersive healthcare simulation units. Throughout June 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “immersive metaverse experiences in decentralized 3D virtual clinical spaces” + “artificial intelligence-driven diagnostic algorithms,” “wearable Internet of Medical Things sensor devices,” and “healthcare modeling and simulation tools.” As research published in 2022 was inspected, only 158 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, I selected 29 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR.

Keywords: metaverse; virtual; diagnostic; Internet of Medical Things; healthcare; sensor

How to cite: Perkins, J. (2022). “Immersive Metaverse Experiences in Decentralized 3D Virtual Clinical Spaces: Artificial Intelligence-driven Diagnostic Algorithms, Wearable Internet of Medical Things Sensor Devices, and Healthcare Modeling and Simulation Tools,” American Journal of Medical Research 9(2): 89–104. doi: 10.22381/ajmr9220226.

Received 28 July 2022 • Received in revised form 26 October 2022
Accepted 29 October 2022 • Available online 30 October 2022

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