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Digital Twins are a Key Enabling Technology for Personalized Medicine.

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Digital twins (DTs) offer a promising avenue for personalized health management and recovery. This perspective explores DT development opportunities for mathematical modelers, considering both academic and commercial applications.

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Area of Science:

  • Biotechnology and Biomedical Engineering
  • Computational Biology
  • Mathematical Modeling

Background:

  • Digital twins (DTs) are computational models of biological systems used for health management.
  • DTs are increasingly bridging the gap from research to clinical application.
  • The technology has broad applications beyond healthcare, including ecology and agriculture.

Purpose of the Study:

  • To highlight digital twin technology as a significant opportunity for the mathematical modeling community.
  • To examine digital twin development from both academic and commercial perspectives.
  • To provide a comprehensive overview of the current landscape and future potential of DTs.

Main Methods:

  • Review of current digital twin applications in health and biology.
  • Analysis of academic and commercial development pathways for DTs.
  • Synthesis of insights from diverse fields including ecology, biotechnology, and agriculture.

Main Results:

  • Digital twins represent a rapidly growing field with significant potential for innovation.
  • Cross-disciplinary applications demonstrate the versatility and impact of DT technology.
  • The integration of mathematical modeling is crucial for advancing DT capabilities.

Conclusions:

  • Digital twins offer a powerful platform for personalized medicine and health maintenance.
  • Collaboration between academia and industry is key to realizing the full potential of DTs.
  • Further research and development in mathematical modeling will drive future advancements in DT technology.