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The digital twin (DT) is an emerging technology that builds on the convergence of computer science, mathematics, engineering, and the life sciences. Given the multiscale nature of biological structures and their environment, biomedical DTs can represent molecules, cells, tissues, organs, systems, patients, and populations and can include aspects from across the modeling and simulation ecosystem. DTs have the potential advance biomedical research with applications for personalized medicine, pharmaceutical development, and clinical trials.
On January 30, 2023, the National Academies of Sciences, Engineering, and Medicine hosted a workshop to discuss the definitions and taxonomy of DTs within the biomedical field, current methods and promising practices for DT development and use as various levels of complexity, key technical challenges and opportunities in the near and long term for DT development and use, and opportunities for translation of promising practices from other field and domains. Workshop panelists highlighted key challenges and opportunities for medical DTs at varying scales, including the varied visions and challenges for DTs, the trade-offs between embracing or simplifying complexity in DTs, the unique spatial and temporal considerations that arise, the diversity of models and data being used in DTs, the challenges with connecting data and models across scales, and implementation issues surrounding data privacy in DTs. This publication summarizes the presentation and discussion of the workshop.
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