JMIR Publications

ABSTRACT

Background:

Digital twins (DTs) are virtual representations of physical systems, enabling real-time simulation, predictive modeling, and optimization. In healthcare, DTs hold transformative potential in personalized medicine, operational efficiency, and medical research. However, their integration is hindered by data-related challenges, ethical concerns, and socioeconomic barriers.

Objective:

This systematic meta-review aims to map primary applications of DTs in healthcare, identify implementation challenges and limitations, and highlight gaps to guide future research.

Methods:

A systematic meta-review was conducted following PRISMA guidelines for scoping reviews. Twenty-five literature reviews published between 2021 and 2024 were included, sourced from PubMed, Web of Science, PsycInfo, and Embase. Thematic synthesis was used to categorize applications, stakeholders, and barriers to adoption.

Results:

DTs in healthcare are primarily used in three domains: personalized medicine (e.g., predictive diagnostics, patient-specific treatment simulations), operational efficiency (e.g., hospital resource optimization), and medical research (e.g., virtual clinical trials and drug discovery). Barriers include issues related to data quality, scalability, ethical and regulatory concerns, and financial constraints. Research gaps were identified in achieving scalability, interoperability, and robust clinical validation.

Conclusions:

While digital twins have transformative potential to revolutionize healthcare by enabling individualized care, streamlining operations, and advancing research, their adoption faces significant challenges. Addressing these requires interdisciplinary efforts, standardized protocols, and inclusive strategies to promote equitable access and ensure meaningful healthcare impact.