Digital Twins for More Precise and Personalized Treatment

Area of Science:

• Digital Health
• Medical Informatics
• Computational Medicine

Background:

• Digital Twins (DTs), digital replicas of physical entities, are widely used in manufacturing.
• Current applications of DTs in healthcare are limited.
• Increasing demand for precise and personalized medical treatments necessitates exploring new technologies like DTs.

Purpose of the Study:

• To explore the potential for creating and utilizing Digital Twins in a healthcare context.
• To demonstrate the proof-of-concept for DTs using synthetic patient data.
• To understand the possibilities and challenges associated with applying DTs for enhanced clinical decision support.

Main Methods:

• Development of Digital Twins (DTs) for synthetic patients using computer-generated data.
• Utilized historical synthetic patient data to create current synthetic patient DTs.
• Conducted a numerical experiment in a synthetic environment to evaluate the approach.

Main Results:

• Successfully created Digital Twins (DTs) of synthetic patients.
• The study provides a foundation for understanding the strengths and weaknesses of DTs in healthcare.
• Identified potential challenges and possibilities for DT implementation in clinical settings.

Conclusions:

• Digital Twins (DTs) offer potential for advancing personalized medicine and clinical decision support.
• Further research is needed to overcome challenges and realize the full benefits of DTs in real-world healthcare scenarios.
• This proof-of-concept highlights the importance of exploring DTs for future healthcare innovations.