Digital twins as global learning health and disease models for preventive and personalized medicine

Ineffective medication is a major healthcare problem causing significant patient suffering and economic costs. This issue stems from the complex nature of diseases, which involve altered interactions among thousands of genes across multiple cell types and organs. Disease progression can vary between patients and over time, influenced by genetic and environmental factors. To address this challenge, digital twins have emerged as a promising approach, which have led to international initiatives aiming at clinical implementations. Digital twins are virtual representations of health and disease processes that can integrate real-time data and simulations to predict, prevent, and personalize treatments. Early…

• AH
  American Heart Association

  Awards: AHA957729, AHAMERIT1185447
• KI
  Karolinska Institutet
• NI
  National Institute of Nursing Research

  Awards: U01 HG007691, R01 HL166137, R01 HL1551107