ABSTRACT. The objective of this paper is to systematically review real-time patient healthcare data monitoring, precise diagnosis and treatment, immersive medical procedures and interventions, and personalized medical image-based patient surgical conditions. The findings and analyses highlight that virtual medical and human‒computer interaction technologies improve immersive virtual healthcare, medical diagnosis services, remote patient assessment, and disease prevention and treatment, shaping treatment planning. Throughout June 2024, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “immersive medical procedures and interventions” + “remote sensing and wearable healthcare technologies,” “deep and machine learning-based medical imaging data,” and “real-time 3D virtual diagnosis and treatment simulations.” As research published between 2022 and 2024 was inspected, only 167 articles satisfied the eligibility criteria, and 28 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: BIBOT, CASP, DistillerSR, METAGEAR package for R, PICO Portal, and SRDR+.

Keywords: remote sensor; wearable healthcare; deep and machine learning-based medical imaging data; real-time 3D virtual diagnosis and treatment simulation; immersive medical procedure and intervention

How to cite: Almășan, A. I., and Țucmeanu, A.-I. (2024). “Remote Sensing and Wearable Healthcare Technologies, Deep and Machine Learning-based Medical Imaging Data, and Real-Time 3D Virtual Diagnosis and Treatment Simulations for Immersive Medical Procedures and Interventions,” American Journal of Medical Research 11(2): 55–70. doi: 10.22381/ajmr11220244.

Received 12 July 2024 • Received in revised form 21 October 2024
Accepted 25 October 2024 • Available online 30 October 2024