Advancing Health Care With Digital Twins: Meta-Review of Applications and Implementation Challenges.

Area of Science:

• Digital Health
• Biomedical Informatics
• Health Systems Engineering

Background:

• Digital twins (DTs) are emerging as powerful tools for simulating and optimizing real-world systems.
• Their application in healthcare promises advancements in personalized medicine, operational efficiency, and medical research.
• Integration faces hurdles including fragmented systems, ethical considerations, and adoption barriers.

Purpose of the Study:

• Systematically review literature on digital twin applications in healthcare.
• Map primary DT applications and identify key challenges and limitations.
• Highlight research gaps to guide future development and implementation.

Main Methods:

• Conducted a meta-review following PRISMA-ScR guidelines.
• Included 25 literature reviews published between 2021-2024 from 5 major databases.
• Utilized thematic synthesis to categorize applications, stakeholders, and barriers.

Main Results:

• Identified 3 primary DT applications: personalized medicine, operational efficiency, and medical research.
• Current applications like predictive diagnostics and treatment simulations show significant potential but are nascent.
• Key challenges include data quality, ethical concerns, socioeconomic barriers, scalability, interoperability, and clinical validation.

Conclusions:

• Digital twins possess transformative potential for individualized healthcare, optimized operations, and accelerated research.
• Adoption is currently impeded by technical, ethical, and financial barriers.
• Interdisciplinary collaboration, standardization, and inclusive strategies are crucial for equitable and impactful DT implementation.