The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Digital twins for health: a scoping review
Open accessAbstract
The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology.
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Cites
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“Around 2010,NASA and John Vickers utilizedthe DT as a virtual model of a physical system 5,6 .”
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“Academic research platforms, such as Beiwe 70 and Mindlamp 71 are now being used to develop personalized modelsthatenable behavioral and psychological interventions that can be delivered in real-time 72 .”
BackgroundDigital Phenotyping: Technology for a New Science of Behavior
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A Novel Cloud-Based Framework for the Elderly Healthcare Services Using Digital Twin
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Digital twins to personalize medicine
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Cited by
How to build the virtual cell with artificial intelligence: Priorities and opportunities
Bunne, C., Roohani, Y., Rosen, Y., Gupta, A., Zhang, X., Roed, M., Alexandrov, T., AlQuraishi, M., Brennan, P., Burkhardt, D. B., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.015 · Cited by 393
“The AIVC could introduce a new approach to diagnostics that incorporates a personalized AIVC (or a digital twin 139 ) to track a patient’s health and suggest suitable interventions.”
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Advancing health care with digital twins: meta-review of applications and implementation challenges
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Harnessing EHR data for health research
Tang, A. S., Woldemariam, S. R., Miramontes, S., Norgeot, B., Oskotsky, T. T., & Sirota, M. (2024) · Nature Medicine · doi:10.1038/s41591-024-03074-8 · Cited by 87
Digital twin systems for musculoskeletal applications: A current concepts review
Diniz, P., Grimm, B., Garcia, F., Fayad, J., Ley, C., Mouton, C., Oeding, J. F., Hirschmann, M. T., Samuelsson, K., & Seil, R. (2025) · Knee Surgery, Sports Traumatology, Arthroscopy · doi:10.1002/ksa.12627 · Cited by 60
Metadata
- Authors
- Katsoulakis, E., Wang, Q., Wu, H., Shahriyari, L., Fletcher, R., Liu, J., Achenie, L., Liu, H., Jackson, P., Xiao, Y., Syeda-Mahmood, T., Tuli, R., & Deng, J.
- Venue
- npj Digital Medicine
- Published
- 2024-03-22
- Volume
- 7
- Issue
- 1
- Pages
- 77-77
- ISSN
- 2398-6352 (ISSN-L)
- OpenAlex
- W4393094536
- Cited by
- 760
- References
- 61
- FWCI
- 60.77
- Type
- journal article
- Language
- en
- Open access
- gold
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- Katsoulakis, E., Wang, Q., Wu, H., Shahriyari, L., Fletcher, R., Liu, J., Achenie, L., Liu, H., Jackson, P., Xiao, Y., Syeda-Mahmood, T., Tuli, R., & Deng, J. (2024). Digital twins for health: a scoping review. npj Digital Medicine. https://doi.org/10.1038/s41746-024-01073-0
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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