Advancing health care with digital twins: meta-review of applications and implementation challenges
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Abstract
Background: Digital twins (DTs) are digital representations of real-world systems, enabling advanced simulations, predictive modeling, and real-time optimization in various fields, including health care. Despite growing interest, the integration of DTs in health care faces challenges such as fragmented applications, ethical concerns, and barriers to adoption. Objective: This study systematically reviews the existing literature on DT applications in health care with three objectives: (1) to map primary applications, (2) to identify key challenges and limitations, and (3) to highlight gaps that can guide future research. Methods: A meta-review was conducted in a systematic fashion, adhering to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, and included 25 literature reviews published between 2021 and 2024. The search encompassed 5 databases: PubMed, CINAHL, Web of Science, Embase, and PsycINFO. Thematic synthesis was used to categorize DT applications, stakeholders, and barriers to adoption. Results: A total of 3 primary DT applications in health care were identified: personalized medicine, operational efficiency, and medical research. While current applications, such as predictive diagnostics, patient-specific treatment simulations, and hospital resource optimization, remain in their early stages of development, they highlight the significant potential of DTs. Challenges include data quality, ethical issues, and socioeconomic barriers. This review also identified gaps in scalability, interoperability, and clinical validation. Conclusions: DTs hold transformative potential in health care, providing individualized care, operational optimization, and accelerated research. However, their adoption is hindered by technical, ethical, and financial barriers. Addressing these issues requires interdisciplinary collaboration, standardized protocols, and inclusive implementation strategies to ensure equitable access and meaningful impact.
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The shifts in engagement are likely part of a broader evolution in health care, driven by rapid technological advancements.
“Rather than attributing the improvement solely to the pandemic, it is likely that these shifts are part of a broader evolution in health care, driven by rapid technological advancements [44].”
From Evolving Medical Students’ Digital Health Perceptions and Intentions: Insights From a Prepandemic and Postpandemic Survey Study (2025) · cites this paper as [44]
The Discussion’s Conclusions describe digital twins as a potentially transformative health care technology, supporting the broad point that technological advances may change care. The paper does not establish that the cited shifts in engagement are part of that evolution or compare technological change with the pandemic as an explanation (no page numbers are available in our copy). (LLM, conf=0.80)
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Digital Twins in Translational Research and Health Care: An Anthropological Perspective
Wagner, J. K. (2026) · Ethics & Human Research · doi:10.1002/eahr.70022
AI-Generated Digital Twins for Cognitive Disease Simulation
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Artificial Intelligence in Rare Diseases: Workflow-Integrated Precision Kidney Care
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The Future of Digital Twins in Healthcare: Results from an Expert Survey
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One Health Cyber-Biotechnology for Nano-Plastic Intelligence Systems and Logic Integration
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Structural requirements for intelligent clinical digital twins in feedback-driven care
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From prediction to responsibility and care in predictive medicine (Preprint)Preprint
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Digital Twin Security for Smart Healthcare Systems
Syed Anwar Hussainy, F., Basheer, S., Kumar, S. J. N., Priya, N., Bhanumathi, M., & Deepan, S. (2026) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc70182.2026.11687543
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- Authors
- Ringeval, M., Etindele Sosso, F. A., Cousineau, M., & Paré, G.
- Affiliations
- HEC Montréal (CA)
- Corresponding author
- Ringeval, Mickaël
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2025-02-19
- Volume
- 27
- Pages
- e69544
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC11888003
- OpenAlex
- W4406790909
- Cited by
- 108
- References
- 40
- Citations by year
- 2026: 83 · 2025: 25
- FWCI
- 6.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Keywords
- digital twin, health care applications, personalized medicine, hospital operational efficiency, predictive diagnostics, hospital resource optimization, implementation barriers, data quality, interoperability, clinical validation, scalability, barriers to adoption
- MeSH terms
- Delivery of Health Care, Humans, Precision Medicine
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