Digital twins in healthcare: A systematic review of current applications, frameworks, and future directions
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By
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Valeria Calcaterra
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Luca Guardamagna
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Alessandro Gatti
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Virginia Rossi
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Pamela Patanè
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Luca Marin
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Matteo Vandoni
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Gianvincenzo Zuccotti
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June 23, 2026
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Clinical Scorecard: Exploring Digital Twins in Medicine: A Comprehensive Review of Existing Uses, Models, and Prospective Developments
At a Glance
| Category | Detail |
|---|
| Condition | Digital Twins in Healthcare |
| Key Mechanisms | Real-time data integration and analysis, predictive modeling, and personalized treatment strategies. |
| Target Population | Patients with chronic diseases and those requiring personalized medical care. |
| Care Setting | Clinical practice and healthcare systems. |
Key Highlights
- Digital twins enable high-fidelity modeling of individual patients.
- Applications include chronic disease management, surgical planning, and rehabilitation.
- DTs facilitate personalized diagnostics and treatment planning.
- Integration of DTs promotes coordinated, multidisciplinary patient care.
- Challenges include data integration and model fidelity.
Guideline-Based Recommendations
Diagnosis
- Utilize digital twins for personalized diagnostics.
Management
- Implement digital twins in chronic disease management and treatment optimization.
Monitoring & Follow-up
- Employ continuous monitoring through digital twin technology.
Risks
- Address challenges related to data integration and model fidelity.
Patient & Prescribing Data
Individuals with complex medical conditions requiring personalized approaches.
Digital twins allow for data-driven experimentation and treatment strategy optimization.
Clinical Best Practices
- Incorporate multimodal data sources for effective digital twin modeling.
- Foster a multidisciplinary approach to patient care using digital twins.
Related Resources & Content
