Dynamic Digital Twin: Diagnosis, Treatment, Prediction, and Prevention of Disease During the Life Course

Skander Tahar Mulder, Amir-Houshang Omidvari, Anja J. Rueten-Budde, Rihan Hai, Can Akgün, David M.J. Tax, M.J.T. Reinders, Marcel Reinders, Valentijn Visch, More Authors

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
233 Downloads (Pure)


A digital twin (DT), originally defined as a virtual representation of a physical asset, system, or process, is a new concept in health care. A DT in health care is not a single technology but a domain-adapted multimodal modeling approach incorporating the acquisition, management, analysis, prediction, and interpretation of data, aiming to improve medical decision-making. However, there are many challenges and barriers that must be overcome before a DT can be used in health care. In this viewpoint paper, we build on the current literature, address these challenges, and describe a dynamic DT in health care for optimizing individual patient health care journeys, specifically for women at risk for cardiovascular complications in the preconception and pregnancy periods and across the life course. We describe how we can commit multiple domains to developing this DT. With our cross-domain definition of the DT, we aim to define future goals, trade-offs, and methods that will guide the development of the dynamic DT and implementation strategies in health care.

Original languageEnglish
Article numbere35675
Number of pages11
JournalJournal of Medical Internet Research
Issue number9
Publication statusPublished - 2022


  • artifical intelligence
  • cardiovascular
  • digital health
  • digital twin
  • disease
  • health
  • machine learning
  • obstetrics


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