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Area of Science:

Computational biology
Biophysics
Medical informatics

Background:

Growing global interest in virtual human models for clinical applications.
Digital twins, personalized virtual models with real-time data, are emerging.
Existing models lack comprehensive physiological integration and physical constraints.

Purpose of the Study:

To outline the requirements for a physics-based, comprehensive virtual human model.
To discuss the potential of personalized digital twins in healthcare.
To highlight progress and future directions for integrated human modeling.

Main Methods:

Conceptual framework development.
Review of the IUPS Physiome Project.
Discussion of multiscale integration from systems physiology to protein function.

Main Results:

A proposed virtual human model framework is detailed.
The importance of physics-based constraints and comprehensive knowledge graphs is emphasized.
The potential for personalization and clinical record linkage is explored.

Conclusions:

A physics-constrained, comprehensive, and multiscale virtual human model is achievable.
Such models, especially personalized digital twins, hold significant promise for advancing clinical diagnosis and treatment.
Further development requires a robust framework and integration of diverse data sources.