ABSTRACT. This article reviews and advances existing literature concerning medical imaging-based computer-assisted treatments, immersive healthcare services, and digital twin-based medical condition diagnosis. Throughout July 2024, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “digital twin-based medical condition diagnosis” + “artificial intelligence-driven clinical data analytics,” “machine learning-based diagnosis and treatment data,” and “medical decision support and patient-centered smart healthcare systems.” As research published between 2022 and 2024 was inspected, only 176 articles satisfied the eligibility criteria, and 30 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: ASReview Lab, Catchii, Eppi-Reviewer, JBI SUMARI, Litstream, and Nested Knowledge.

Keywords: artificial intelligence-driven clinical data analytics; machine learning-based diagnosis and treatment data; medical decision support; patient-centered smart healthcare; digital twin-based medical condition diagnosis

How to cite: Popescu, G. H., Csillag, P., Poliakova, A., and Alpopi, Ș. G. (2024). “Artificial Intelligence-driven Clinical Data Analytics, Machine Learning-based Diagnosis and Treatment Data, and Medical Decision Support and Patient-centered Smart Healthcare Systems for Digital Twin-based Medical Condition Diagnosis,” American Journal of Medical Research 11(2): 7–22. doi: 10.22381/ajmr11220241.

Received 6 August 2024 • Received in revised form 20 October 2024
Accepted 24 October 2024 • Available online 30 October 2024