arXiv:2606.11264v1 Announce Type: cross Abstract: Health digital twins (HDTs) promise patient-specific modeling and decision support but current approaches remain structurally fragmented: monolithic models that address a single organ or task lack cross-scale fidelity, while system-level twins lack generalizable architectural frameworks. We propose OmniBioTwin, a System-of-Twinned-Systems (SoTS) framework that organizes HDTs as modular computational entities coupled through explicit interaction operators within a multi-layer network architecture. The framework comprises seven coordinated layers
The proliferation of digital health data and advances in AI and biological modeling are converging, making sophisticated, patient-specific digital twins a near-term possibility.
This framework addresses fragmentation in health digital twins, potentially enabling more accurate, personalized medicine and proactive health management at scale.
Current fragmented approaches to health digital twins could be superseded by an integrated, multi-layer framework, offering a more holistic view of patient health.
- · Personalized medicine developers
- · Healthcare providers
- · AI in healthcare companies
- · Patients
- · Fragmented health tech solutions
- · One-size-fits-all medical approaches
Patients will receive more precise diagnoses and treatment plans tailored to their unique biological profiles.
The integration of various organ- or task-specific digital twins will lead to new insights into systemic health interactions and disease progression.
Predictive healthcare models could become highly accurate, shifting focus from treatment to proactive prevention and lifespan extension.
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Read at arXiv cs.AI