AI-enabled Digital Twins in Healthcare

This Research Topic has been initiated by the board members of the Digital Twins for Health Society (About DT4HS), a mission-driven society advancing healthcare and medicine through excellence in science, education, outreach, and community engagement in human digital twins.

Digital twins (DTs), now prevalent across many fields, are seeing a surge of interest within healthcare. The vision of digital twins for health (DT4H) offers immense potential to revolutionize every aspect of the healthcare system, including its management, the delivery of care, and the maintenance of well-being. This progress is fueled by the growth of big data and continuous advancements in artificial intelligence (AI), machine learning, and high-performance computing, which provide the necessary expertise, theory, algorithms, and infrastructure to accelerate digital twin development. However, while digital twin research and development are underway in other sectors, DT4H is still in its early stages.


Goal

Recent advances in AI, machine learning, multiscale modeling, and high-performance computing are driving the rapid development of digital twins in healthcare. The goal of this Research Topic is to present pioneering research and development in the realm of DT technology, fostering an international synergy among all stakeholders. We envision the ongoing development of these DT technologies would lead to enhanced healthcare and improved quality of life for millions of people worldwide.


Scope and Information for authors

In this Research Topic, we welcome original contributions from the scientific community in the format of research articles, perspectives, and reviews. Potential topics include, but are not limited to:

• Multimodal real-world data integration, curation, standard, and management
• Multi-omics data and biomarker discovery in healthcare
• Mathematical, statistical, and mechanistic modeling of organs and systems
• Physics-informed machine learning
• Modeling and simulations of time-series data
•Natural language processing of electronic health records and clinical notes
• Digital twin application in clinical trials
• AI-assisted clinical decision support
• Pharmacokinetics-Pharmacodynamics modeling of drug dynamics

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Digital Twins, Artificial Intelligence, Machine Learning, Multimodal Data, Predictive Modeling, Precision Health

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.