ABSTRACT. The aim of this systematic review is to synthesize and analyze metaverse smart health monitoring and medical treatment processes, remote treatment plans and consultations, and long-term predictive medical diagnosis. In this research, prior findings were cumulated indicating that metaverse-based personal healthcare services, distributed machine learning algorithms, and healthcare artificial intelligence systems can be leveraged in spatio-temporal virtual worlds and immersive digital spaces. A quantitative literature review of ProQuest, Scopus, and the Web of Science was carried out throughout June 2023, with search terms including “Internet of Things-based disease prevention and management in the healthcare metaverse” + “digital twin-based medical diagnosis and clinical practice,” “deep learning-based healthcare data analysis,” and “Internet of Things-based disease prevention and management.” As research published between 2022 and 2023 was inspected, only 151 articles satisfied the eligibility criteria, and 18 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, Distiller SR, ROBIS, and SRDR.

Keywords: digital twin; medical diagnosis and clinical practice; deep learning-based healthcare data analysis; Internet of Things-based disease prevention and management; healthcare metaverse

How to cite: Taylor, E. (2023). “Digital Twin-based Medical Diagnosis and Clinical Practice, Deep Learning-based Healthcare Data Analysis, and Internet of Things-based Disease Prevention and Management in the Healthcare Metaverse,” American Journal of Medical Research 10(2): 37–51. doi: 10.22381/ajmr10220233.

Received 5 July 2023 • Received in revised form 22 October 2023
Accepted 26 October 2023 • Available online 30 October 2023