Digital Twins for Biofluids
Clinical Snapshot
PICO Framework
| P — Population | Biomedical engineering applications involving biofluids (blood, cerebrospinal fluid, lymphatic fluid, etc.) |
| I — Intervention | Digital twin technology - virtual representations dynamically linked to physical biofluid systems |
| C — Comparator | Traditional static modeling approaches or conventional clinical assessment methods |
| O — Outcomes | Real-time prediction capabilities, optimization of therapeutic planning, personalization of healthcare interventions, diagnostic accuracy, device design improvements |
Bottom Line
This narrative review examines digital twin technology for biofluid applications, representing virtual systems dynamically linked to patient physiology for real-time monitoring and prediction. While the concept shows promise for personalized medicine, diagnostics, and therapeutic planning, significant challenges remain in integrating complex physics-based models with clinical data. The technology could transform biomedical engineering by enabling real-time optimization of treatments, but implementation faces hurdles including computational complexity, data standardization, and regulatory approval. For Australian clinicians, this represents an emerging technology that may eventually enhance patient care in cardiovascular, neurological, and critical care settings, though practical clinical applications remain largely developmental. The review highlights the need for standardized protocols and better integration methods before widespread clinical adoption.
Key Findings
P Value: Not applicable for narrative review
Effect Size: Not quantified - qualitative assessment of technological capabilities
Primary Outcome: Integration of physics-based models with clinical data for real-time biofluid system monitoring and prediction
Nnt Or Sensitivity: Not reported - technology assessment rather than clinical trial
Confidence Interval: Not applicable for narrative review
Clinical Application
Currently limited by computational requirements, data integration challenges, and lack of standardized protocols Relevant to Australian healthcare system with potential applications in major teaching hospitals and research institutions. May require TGA evaluation for clinical implementation and consideration for PBS funding of associated technologies Patients requiring biofluid monitoring including cardiovascular, neurological, and critical care populations
Abstract
Digital twins-virtual representations dynamically linked to physical systems-have the potential to transform biomedical engineering by enabling real-time prediction, optimization, and personalization in health and disease. In biofluids, digital twins offer a framework for integrating physics-based models with data from clinical imaging, sensors, and physiological measurements to support diagnostics, therapeutic planning, and device design. This article reviews modeling approaches used in the construction of digital twins for biofluid applications. We survey high-fidelity numerical methods alongside emerging machine learning techniques, highlighting their respective strengths and limitations. Key requirements for digital twins are discussed, emphasizing the bidirectional interaction between physical and virtual assets and the importance of selecting modeling strategies tailored to specific biomedical contexts. While notable progress has been made over the past decade, significant challenges remain, particularly in integrating multiphysics models with data-driven methods and in establishing standardized protocols for data acquisition, interoperability, and sharing.
References
- 1.Figueroa, C. A., Garikipati, K., Yang, H., & Capecelatro, J. (2026). Digital twins for biofluids. Annual Review of Biomedical Engineering, 28. https://doi.org/10.1146/annurev-bioeng-081325-031501
Related Research
IEEE transactions on cybernetics
Knowledge Distillation and Reinforcement Learning in a Human–Machine Collaboration Delivery System With a Robotic Arm
3 Aug 2026
Cognitive, affective & behavioral neuroscience
Emotion detection unveiled: A cognitive-computational synthesis of physiological models, machine learning, and datasets.
3 Aug 2026
Computational biology and chemistry
The intervention of AI and ML generated Xenobots in the perspective of future technology: a review.
3 Aug 2026
This content is for educational purposes for healthcare professionals only and does not constitute clinical advice. Clinical decisions should be based on individual patient assessment, current guidelines, and appropriate specialist consultation. Editorial Standards · Privacy Policy · Terms of Service