Digital twins to personalize medicine
Open accessAbstract
Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient.
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In healthcare, digital twins are a promising tool for drug target discovery.
“In healthcare, digital twins are a promising tool for drug target discovery 65,66 .”
From Multimodal biomedical AI (2022) · cites this paper as [65]
This will likely require large collaborations across health systems, research groups and industry.
“This will likely require large collaborations across health systems, research groups and industry, such as the Swedish Digital Twins Consortium 65,72 .”
From Multimodal biomedical AI (2022) · cites this paper as [65]
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Wickramasinghe, N. S., Ulapane, N. N. B., Andargoli, A., Ossai, C. I., Shuakat, M. N., Nguyen, T. A., & Zelcer, J. (2022) · JAMIA Open · doi:10.1093/jamiaopen/ooac072 · Cited by 52 · see citing works
Electrical Impedance Tomography Guided by Digital Twins and Deep Learning for Lung Monitoring
Zhu, L., Lu, W., Soleimani, M., Li, Z., & Zhang, M. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3298389 · Cited by 51 · see citing works
Kirienko, M., Sollini, M., Ninatti, G., Loiacono, D., Giacomello, E., Gozzi, N., Francesco Amigoni, A., Mainardi, L. T., Lanzi, P. L., & Chiti, A. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05339-7 · Cited by 51 · see citing works
Medical Digital Twin: A Review on Technical Principles and Clinical Applications
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Study on the Applicability of Digital Twins for Home Remote Motor Rehabilitation
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Machine Learning in Clinical Trials: A Primer with Applications to Neurology
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Introduction of a comprehensive Structure Model for the Digital Twin in Manufacturing
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- Authors
- on behalf of the Swedish Digital Twin Consortium, Björnsson, B., Borrebaeck, C., Elander, N., Gasslander, T., Gawel, D. R., Gustafsson, M., Jörnsten, R., Lee, E. J., Li, X., Lilja, S., Martínez-Enguita, D., Matussek, A., Sandström, P., Schäfer, S., Stenmarker, M., Sun, X. F., Sysoev, O., Zhang, H., & Benson, M.
- Affiliations
- Lund University (SE)
- Linköping University (SE)
- Chalmers University of Technology (SE)
- Karolinska University Hospital (SE)
- Futurum - Akademin för Hälsa och Vård (SE)
- Corresponding author
- Benson, Mikael
- ORCID
- Venue
- Genome Medicine
- Published
- 2019-12-31
- Volume
- 12
- Issue
- 1
- Pages
- 4-4
- ISSN
- 1756-994X (ISSN-L)
- PMC
- PMC6938608
- OpenAlex
- W2999365564
- Cited by
- 566
- References
- 9
- Citations by year
- 2026: 143 · 2025: 126 · 2024: 103 · 2023: 79 · 2022: 66 · 2021: 37 · 2020: 12
- FWCI
- 19.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DiVA (Linkoping University) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Lund University Publications (Lund University) (submittedVersion)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Funders
- Swedish Cancer Foundation
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- digital twin, personalized medicine, patient modeling, drug repurposing, computational drug screening, precision medicine, data integration
- MeSH terms
- Disease, Humans, Databases, Factual, Neural Networks, Computer, Precision Medicine
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