Digital Twin in Medicine and Health

The concept of a digital twin originated in industry and manufacturing. Its basic idea is to create a virtual representation of a physical or biological system to simulate realistic data that inform reasoning and help in decision-making. While, so far, a digital twin has been used predominantly for problems in engineering, it is believed that a digital twin also holds great promise for applications in medicine and health.

The concept of a digital twin for simulating molecular data in medicine is largely unexplored. For this reason it is of great interest to establish protocols for simulation, analysis and decision making in a disease-specific manner. This includes also a clarification of differences to simulations, e.g, in systems biology conducted since decades in order to identify the unique benefit of a digital twin.

We are particularly interested in accommodating contributions that explore any of the following research topics (but not limited to):

• AI methods for digital twin
• data integration of mixed data
• definition of digital twin in medicine and health
• machine learning paradigms enabled by digital twin
• multiscale modeling
• multi-omics
• performance improvement due to digital twin
• real-time modeling
• statistical inference with digital twin
• virtual disease-models
• virtual drug-response
• virtual pharmacogenomics

The above topics can be studied for any disease that is within the realm of molecular medicine or pharmacogenomics. Aside from research papers, we welcome also review, perspective and opinion articles.

The purpose of our research topic ‘AI Applications of Deep learning in Computational Social Science’ is to bring together researchers from university and industry interested in this interdisciplinary topic to share information about recent progress and persisting challenges.

This research topic is intended for Frontiers in Molecular Medicine, subsection Bioinformatics and Artificial Intelligence for Molecular Medicine.

Keywords: Digital twin, medicine, health, Deep learning, Data integration, Digital twin modeling, Explainable models and XAI, Hybrid models, New learning paradigms, Mathematical, statistical, and mechanistic modeling, statistical inference

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.