Digital Twins in Healthcare: A New Frontier for Personalized Treatments

Digital health innovation has accelerated dramatically in recent years, with digital twins emerging as a transformative force in the pursuit of personalized healthcare. While traditional care pathways often depend on generalized protocols and population-level data, such approaches can insufficiently address the unique nuances that exist between individual patients, resulting in variable clinical outcomes. The growing adoption of digital twins—dynamic, virtual representations of an individual’s physiological and clinical profile—offers new opportunities to integrate complex, multi-modal data, including imaging, physiological signals, electronic healthcare records (EHR), omics, and lifestyle information, into real-time, patient-specific simulations/emulations. Recent research has shown that digital twins can enhance the accuracy of disease modeling, improve personalization of treatment regimens, and support predictive analytics. Despite these promising developments, formidable challenges remain in harmonizing diverse datasets, validating these digital representations across varying populations, and seamlessly integrating digital twin models into everyday clinical practice.
This Research Topic aims to explore and highlight the cutting edge of digital twins as a foundation for personalized treatments in healthcare. The main objective is to investigate how patient-specific modeling and simulation technologies can drive new standards of diagnostic precision, therapeutic planning, and outcome prediction across the healthcare continuum. By fostering collaboration among computational scientists, clinicians, and engineers, the Research Topic seeks to identify innovative methodologies, evaluate translational opportunities, and scrutinize the technical, ethical, and regulatory considerations that accompany the broader implementation of digital twins in medicine.
This Research Topic welcomes manuscripts that address, but are not limited to, the following themes:
- Novel techniques for developing and validating digital twin models in various medical domains;
- Integration of diverse patient data (imaging, physiological, EHR, genetic, lifestyle) into virtual healthcare simulations/emulations;
- Evidence for improved diagnostic accuracy, prognostication, and therapeutic personalization enabled by digital twins;
- Technical, ethical, and regulatory hurdles in deploying digital twins within real-world clinical settings;
- Multi-disciplinary adoption of digital twins for individualized healthcare delivery.

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
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... 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
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Digital Twins, Health informatics, Healthcare, Personalization, Digital 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.