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One Patient, Two Worlds: Digital Twins for Everyday Prevention and Care
IEEE Pulse
|January 21, 2026
Summary
Digital twins (DTs) create virtual patient replicas using AI and sensor data for early disease detection and personalized treatment. This technology enhances preventive care and ethical innovation in precision medicine.
Area of Science:
- Digital health
- Biomedical informatics
- Artificial intelligence in healthcare
Background:
- Digital twins (DTs) are virtual patient replicas integrating real-time data from sensors and wearables.
- AI algorithms analyze this data for predictive insights into health conditions.
- DTs offer potential for early disease detection and personalized interventions.
Purpose of the Study:
- To explore the evolving role of digital twins in healthcare detection and preventive care.
- To highlight the convergence of sensors, wearables, and AI in clinical practice.
- To discuss the integration of blockchain for secure data exchange and patient consent.
Main Methods:
- Integration of multimodal data streams (sensors, wearables).
- Application of artificial intelligence (AI) for predictive modeling.
- Utilizing blockchain technology for secure data management and consent.
Main Results:
- Digital twins enable real-time virtual patient evolution.
- Demonstrated advances in cardiology, oncology, and metabolic health applications.
- Facilitation of early disease detection and personalized treatment simulations.
Conclusions:
- Digital twins represent a transformative paradigm in healthcare.
- They are key to advancing precision medicine and ethical innovation.
- Addressing privacy, interoperability, and regulatory aspects is crucial for widespread adoption.
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