Digital twins as global learning health and disease models for preventive and personalized medicine
Abstract Ineffective medication is a major healthcare problem causing significant patient suffering and economic costs. This issue stems from the complex nature of diseases, which involve altered interactions among thousands of genes across multiple cell types and organs. Disease progression can var...
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| Format: | Article |
| Language: | English |
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BMC
2025-02-01
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| Series: | Genome Medicine |
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| Online Access: | https://doi.org/10.1186/s13073-025-01435-7 |
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| author | Xinxiu Li Joseph Loscalzo A. K. M. Firoj Mahmud Dina Mansour Aly Andrey Rzhetsky Marinka Zitnik Mikael Benson |
| author_facet | Xinxiu Li Joseph Loscalzo A. K. M. Firoj Mahmud Dina Mansour Aly Andrey Rzhetsky Marinka Zitnik Mikael Benson |
| author_sort | Xinxiu Li |
| collection | DOAJ |
| description | Abstract Ineffective medication is a major healthcare problem causing significant patient suffering and economic costs. This issue stems from the complex nature of diseases, which involve altered interactions among thousands of genes across multiple cell types and organs. Disease progression can vary between patients and over time, influenced by genetic and environmental factors. To address this challenge, digital twins have emerged as a promising approach, which have led to international initiatives aiming at clinical implementations. Digital twins are virtual representations of health and disease processes that can integrate real-time data and simulations to predict, prevent, and personalize treatments. Early clinical applications of DTs have shown potential in areas like artificial organs, cancer, cardiology, and hospital workflow optimization. However, widespread implementation faces several challenges: (1) characterizing dynamic molecular changes across multiple biological scales; (2) developing computational methods to integrate data into DTs; (3) prioritizing disease mechanisms and therapeutic targets; (4) creating interoperable DT systems that can learn from each other; (5) designing user-friendly interfaces for patients and clinicians; (6) scaling DT technology globally for equitable healthcare access; (7) addressing ethical, regulatory, and financial considerations. Overcoming these hurdles could pave the way for more predictive, preventive, and personalized medicine, potentially transforming healthcare delivery and improving patient outcomes. |
| format | Article |
| id | doaj-art-7531dba0e8c842648aaacf835e3786b5 |
| institution | Kabale University |
| issn | 1756-994X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Medicine |
| spelling | doaj-art-7531dba0e8c842648aaacf835e3786b52025-02-09T12:48:42ZengBMCGenome Medicine1756-994X2025-02-0117111410.1186/s13073-025-01435-7Digital twins as global learning health and disease models for preventive and personalized medicineXinxiu Li0Joseph Loscalzo1A. K. M. Firoj Mahmud2Dina Mansour Aly3Andrey Rzhetsky4Marinka Zitnik5Mikael Benson6Medical Digital Twin Research Group, Department of Clinical Sciences Intervention and Technology, Karolinska InstituteBrigham and Women’s Hospital, Harvard Medical SchoolDepartment of Medical Biochemistry and Microbiology, Uppsala UniversityMedical Digital Twin Research Group, Department of Clinical Sciences Intervention and Technology, Karolinska InstituteDepartments of Medicine and Human Genetics, Institute for Genomics and Systems Biology, University of ChicagoDepartment of Biomedical Informatics, Harvard Medical SchoolMedical Digital Twin Research Group, Department of Clinical Sciences Intervention and Technology, Karolinska InstituteAbstract Ineffective medication is a major healthcare problem causing significant patient suffering and economic costs. This issue stems from the complex nature of diseases, which involve altered interactions among thousands of genes across multiple cell types and organs. Disease progression can vary between patients and over time, influenced by genetic and environmental factors. To address this challenge, digital twins have emerged as a promising approach, which have led to international initiatives aiming at clinical implementations. Digital twins are virtual representations of health and disease processes that can integrate real-time data and simulations to predict, prevent, and personalize treatments. Early clinical applications of DTs have shown potential in areas like artificial organs, cancer, cardiology, and hospital workflow optimization. However, widespread implementation faces several challenges: (1) characterizing dynamic molecular changes across multiple biological scales; (2) developing computational methods to integrate data into DTs; (3) prioritizing disease mechanisms and therapeutic targets; (4) creating interoperable DT systems that can learn from each other; (5) designing user-friendly interfaces for patients and clinicians; (6) scaling DT technology globally for equitable healthcare access; (7) addressing ethical, regulatory, and financial considerations. Overcoming these hurdles could pave the way for more predictive, preventive, and personalized medicine, potentially transforming healthcare delivery and improving patient outcomes.https://doi.org/10.1186/s13073-025-01435-7Digital twinsPersonalized medicineData integrationComputational methods |
| spellingShingle | Xinxiu Li Joseph Loscalzo A. K. M. Firoj Mahmud Dina Mansour Aly Andrey Rzhetsky Marinka Zitnik Mikael Benson Digital twins as global learning health and disease models for preventive and personalized medicine Genome Medicine Digital twins Personalized medicine Data integration Computational methods |
| title | Digital twins as global learning health and disease models for preventive and personalized medicine |
| title_full | Digital twins as global learning health and disease models for preventive and personalized medicine |
| title_fullStr | Digital twins as global learning health and disease models for preventive and personalized medicine |
| title_full_unstemmed | Digital twins as global learning health and disease models for preventive and personalized medicine |
| title_short | Digital twins as global learning health and disease models for preventive and personalized medicine |
| title_sort | digital twins as global learning health and disease models for preventive and personalized medicine |
| topic | Digital twins Personalized medicine Data integration Computational methods |
| url | https://doi.org/10.1186/s13073-025-01435-7 |
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