Linux Foundation Public Health (LFPH) presented healthcare digital twins in 2022 as an emerging field for open-source and cross-industry collaboration—not as a clinically proven technology. Its article grouped possible applications into models of people and body systems, individual organs, and healthcare organizations, while emphasizing connected data and simulation as the foundations.
What is a digital twin in healthcare?
LFPH’s August 29, 2022 article describes a digital twin as a virtual model paired dynamically with a physical counterpart. Data from the real-world system can update the model, which may then help explore scenarios, identify potential problems, or inform decisions. Depending on the application, the data and tools could include sensors, analytics, artificial intelligence (AI), cloud computing, or Internet of Things (IoT) devices; not every twin needs all of them. LFPH’s explanation is consistent with the UK Government Office for Science’s broader description of a cyber-physical system connected to its physical counterpart through a two-way flow of appropriately timed data. The government assessment also notes that data protection, ownership, equality, and bias need consideration as adoption grows.
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The distinction from a static 3D image matters: a twin is intended to remain linked to a real-world subject or system and be informed by data over time. The model’s usefulness still depends on the quality and relevance of its data, how it is updated, and whether it has been validated for the decision it is meant to support.
What kinds of healthcare digital twins did LFPH describe?
LFPH organized healthcare examples by the thing being modeled. Its 2022 article illustrates possible uses; it does not provide comparative performance results or establish that the examples remain active today.
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Person or body-system twins
A model may represent a whole person, a body system, or a particular function. LFPH cited the University of Miami’s MLBox system as an example intended to combine biological, clinical, behavioral, and environmental data to inform personalized sleep treatment. That description reflects the 2022 article; it is not evidence here of current availability or clinical effectiveness.
Organ or smaller-unit twins
A twin can represent an organ, part of an organ, or even subcellular or molecular-level function. LFPH pointed to Dassault Systèmes’ Living Heart Project as a model designed to simulate how a human heart responds to implanted cardiovascular devices. The article presents this as an intended simulation use, not proof of improved patient outcomes.
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Healthcare-organization twins
At the organizational level, a model can simulate a hospital or another healthcare institution. LFPH cited Singapore General Hospital in connection with assessing environmental risks, including infectious-disease transmission. The article supplies no validation study or outcome measure for this example.
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|---|---|---|---|
| Person or body system | A person, system, or function | University of Miami’s MLBox; using multiple kinds of data to inform personalized sleep treatment | An intended application, not current status or clinical effectiveness |
| Organ or smaller unit | An organ, part of an organ, or smaller biological function | Dassault Systèmes’ Living Heart Project; simulating responses to implanted cardiovascular devices | A simulation purpose, not demonstrated patient benefit |
| Healthcare organization | A hospital or institution | Singapore General Hospital; considering environmental risks such as infectious-disease transmission | An example without a reported validation study or outcome measure |
How did LFPH connect digital twins to open source?
LFPH’s argument was that healthcare digital twins could benefit from an open-source ecosystem spanning public-health data infrastructure, health equity, cybersecurity, patient engagement, and health-information exchange. The organization’s current homepage describes its mission as building, promoting, and sustaining open-source software to improve global health innovation. LFPH’s homepage provides that current mission context.
In its 2022 article, LFPH situated its work alongside the LF AI and Data Foundation, LF Edge, and Open 3D Foundation, associating them respectively with AI and data, edge and IoT technologies, and real-time 3D simulation. It also said it had established joint membership with the Digital Twin Consortium focused on healthcare and life sciences. These are dated descriptions from that article; they do not confirm current memberships, active implementations, or present project status. The original LFPH article is the source for those claims.
Two statements in the article convey the organizations’ perspective, rather than independent evidence of outcomes. LFPH Executive Director Jim St. Clair said, “Artificial Intelligence (AI), edge computing and digital twins represent the next generation in data transformation and patient engagement.” Open 3D Foundation General Manager Royal O’Brien described the foundation’s role in real-time 3D simulation: “The Open 3D Foundation, along with its partners and community is helping advance 3D digital twin technology by proving an open source implementation that is completely dynamic with no need to preload the media.”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should be assessed before a healthcare twin is used?
A model’s label does not establish that it is accurate or suitable for clinical or operational decisions. The questions differ by application, but several checks follow directly from the nature of a data-linked model and the governance concerns identified by the UK Government Office for Science:
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- Purpose and decision: Define what the twin is intended to simulate or inform, and avoid treating an exploratory model as a validated decision tool.
- Data and update cycle: Identify which data feed the model, how representative and reliable they are, and how quickly the model reflects relevant changes in the physical counterpart.
- Validation: Look for evidence that the model performs adequately for its intended population, setting, and use. The examples in LFPH’s article do not supply that evidence.
- Privacy and ownership: Establish who can access health or operational data, how it is protected, and who controls or owns it.
- Fairness: Consider whether gaps or biases in the data could lead to unequal performance or harm across groups.
- Interoperability and accountability: Clarify how data and models connect with other systems, and who is responsible when the model informs a consequential decision.
The National Academies’ 2024 publication page describes foundational research needs for digital twins, but it does not establish the current status of LFPH’s projects or relationships. Its publication page is useful context for why substantial questions remain around biomedical digital twins.
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