ABSTRACT. In this article, previous research findings were cumulated, indicating that Internet of Things-based healthcare systems can enable virtual reality therapies by integrating metaverse healthcare data and devices for personalized healthcare delivery and experiences in terms of patient monitoring and medical diagnosis. The contribution to the literature on metaverse-based healthcare and physiological response monitoring systems is by showing that clinical machine learning algorithms can configure personalized diagnosis and care in healthcare metaverse services. Throughout January 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “virtual healthcare environments” + “metaverse-based healthcare and physiological response monitoring systems,” “deep and machine learning-based medical imaging data,” and “wearable biomedical sensors.” As research published between 2022 and 2023 was inspected, only 163 articles satisfied the eligibility criteria, and 19 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: AXIS, Dedoose, ROBIS, and SRDR.

Keywords: metaverse; physiological response monitoring; deep and machine learning; medical imaging data; wearable biomedical sensors; virtual healthcare

How to cite: Pera, A. (2023). “Metaverse-based Healthcare and Physiological Response Monitoring Systems, Deep and Machine Learning-based Medical Imaging Data, and Wearable Biomedical Sensors in Virtual Healthcare Environments,” American Journal of Medical Research 10(1): 67–81. doi: 10.22381/ajmr10120235.

Received 16 February 2023 • Received in revised form 23 April 2023
Accepted 27 April 2023 • Available online 30 April 2023