Digital Representation of Patients as Medical Digital Twins: Data-Centric Viewpoint
AbstractPrecision medicine involves a paradigm shift toward personalized data-driven clinical decisions. The concept of a medical “digital twin” has recently become popular to designate digital representations of patients as a support for a wide range of data science applications. However...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2025-01-01
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| Series: | JMIR Medical Informatics |
| Online Access: | https://medinform.jmir.org/2025/1/e53542 |
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| author | Stanislas Demuth Jérôme De Sèze Gilles Edan Tjalf Ziemssen Françoise Simon Pierre-Antoine Gourraud |
| author_facet | Stanislas Demuth Jérôme De Sèze Gilles Edan Tjalf Ziemssen Françoise Simon Pierre-Antoine Gourraud |
| author_sort | Stanislas Demuth |
| collection | DOAJ |
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AbstractPrecision medicine involves a paradigm shift toward personalized data-driven clinical decisions. The concept of a medical “digital twin” has recently become popular to designate digital representations of patients as a support for a wide range of data science applications. However, the concept is ambiguous when it comes to practical implementations. Here, we propose a medical digital twin framework with a data-centric approach. We argue that a single digital representation of patients cannot support all the data uses of digital twins for technical and regulatory reasons. Instead, we propose a data architecture leveraging three main families of digital representations: (1) multimodal dashboards integrating various raw health records at points of care to assist with perception and documentation, (2) virtual patients, which provide nonsensitive data for collective secondary uses, and (3) individual predictions that support clinical decisions. For a given patient, multiple digital representations may be generated according to the different clinical pathways the patient goes through, each tailored to balance the trade-offs associated with the respective intended uses. Therefore, our proposed framework conceives the medical digital twin as a data architecture leveraging several digital representations of patients along clinical pathways. |
| format | Article |
| id | doaj-art-dbd3a6d375894463af6fd8bc9024dc35 |
| institution | Kabale University |
| issn | 2291-9694 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Medical Informatics |
| spelling | doaj-art-dbd3a6d375894463af6fd8bc9024dc352025-02-04T20:31:42ZengJMIR PublicationsJMIR Medical Informatics2291-96942025-01-0113e53542e5354210.2196/53542Digital Representation of Patients as Medical Digital Twins: Data-Centric ViewpointStanislas Demuthhttp://orcid.org/0000-0002-2504-5294Jérôme De Sèzehttp://orcid.org/0000-0002-7197-7578Gilles Edanhttp://orcid.org/0000-0002-9641-6734Tjalf Ziemssenhttp://orcid.org/0000-0001-8799-8202Françoise Simonhttp://orcid.org/0000-0001-9670-5889Pierre-Antoine Gourraudhttp://orcid.org/0000-0003-1131-9554 AbstractPrecision medicine involves a paradigm shift toward personalized data-driven clinical decisions. The concept of a medical “digital twin” has recently become popular to designate digital representations of patients as a support for a wide range of data science applications. However, the concept is ambiguous when it comes to practical implementations. Here, we propose a medical digital twin framework with a data-centric approach. We argue that a single digital representation of patients cannot support all the data uses of digital twins for technical and regulatory reasons. Instead, we propose a data architecture leveraging three main families of digital representations: (1) multimodal dashboards integrating various raw health records at points of care to assist with perception and documentation, (2) virtual patients, which provide nonsensitive data for collective secondary uses, and (3) individual predictions that support clinical decisions. For a given patient, multiple digital representations may be generated according to the different clinical pathways the patient goes through, each tailored to balance the trade-offs associated with the respective intended uses. Therefore, our proposed framework conceives the medical digital twin as a data architecture leveraging several digital representations of patients along clinical pathways.https://medinform.jmir.org/2025/1/e53542 |
| spellingShingle | Stanislas Demuth Jérôme De Sèze Gilles Edan Tjalf Ziemssen Françoise Simon Pierre-Antoine Gourraud Digital Representation of Patients as Medical Digital Twins: Data-Centric Viewpoint JMIR Medical Informatics |
| title | Digital Representation of Patients as Medical Digital Twins: Data-Centric Viewpoint |
| title_full | Digital Representation of Patients as Medical Digital Twins: Data-Centric Viewpoint |
| title_fullStr | Digital Representation of Patients as Medical Digital Twins: Data-Centric Viewpoint |
| title_full_unstemmed | Digital Representation of Patients as Medical Digital Twins: Data-Centric Viewpoint |
| title_short | Digital Representation of Patients as Medical Digital Twins: Data-Centric Viewpoint |
| title_sort | digital representation of patients as medical digital twins data centric viewpoint |
| url | https://medinform.jmir.org/2025/1/e53542 |
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