Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Predictive markers of endometriosis: a future perspective.

Archives of gynecology and obstetrics·2026
Same author

Digital twins in menopause: a roadmap for integrating endocrine dynamics, multisystem physiology, and precision medicine.

Maturitas·2026
Same author

Sante publique (Vandoeuvre-les-Nancy, France)·2026
Same author

Sante publique (Vandoeuvre-les-Nancy, France)·2026
Same author

A mechanistic digital twin of ovarian aging integrating follicular dynamics, mitochondrial decline, and lifestyle perturbations.

Maturitas·2026
Same author

Response to: Definition and literature scope of digital twins in reproductive medicine.

Reproductive biomedicine online·2026

Related Experiment Video

Updated: May 11, 2026

Ordering Single Cells and Single Embryos in 3D Confinement: A New Device for High Content Screening
14:22

Ordering Single Cells and Single Embryos in 3D Confinement: A New Device for High Content Screening

Published on: September 18, 2016

8.0K

Multi-scale digital twins for personalized medicine.

Alexandre Vallée1

  • 1Department of Epidemiology and Public Health, Foch Hospital, Suresnes, France.

Frontiers in Digital Health
|March 4, 2026
PubMed
Summary

Multi-scale digital twins (MSDTs) integrate diverse health data for personalized medicine. Future MSDTs promise dynamic health modeling and tailored interventions, overcoming current limitations.

Keywords:
artificial intelligencebiomedical data integrationcausal inferencecomputational modelingdigital healthethical challengesgraph neural networksmachine learning

More Related Videos

Pre-Implantation Genetic Testing for Aneuploidy on a Semiconductor Based Next-Generation Sequencing Platform
09:30

Pre-Implantation Genetic Testing for Aneuploidy on a Semiconductor Based Next-Generation Sequencing Platform

Published on: August 17, 2022

2.1K
Transperineal Prostate Biopsy Using a Cone-shaped Double-hole Method with Dual-plane Probe Guidance
05:35

Transperineal Prostate Biopsy Using a Cone-shaped Double-hole Method with Dual-plane Probe Guidance

Published on: June 6, 2025

2.6K

Related Experiment Videos

Last Updated: May 11, 2026

Ordering Single Cells and Single Embryos in 3D Confinement: A New Device for High Content Screening
14:22

Ordering Single Cells and Single Embryos in 3D Confinement: A New Device for High Content Screening

Published on: September 18, 2016

8.0K
Pre-Implantation Genetic Testing for Aneuploidy on a Semiconductor Based Next-Generation Sequencing Platform
09:30

Pre-Implantation Genetic Testing for Aneuploidy on a Semiconductor Based Next-Generation Sequencing Platform

Published on: August 17, 2022

2.1K
Transperineal Prostate Biopsy Using a Cone-shaped Double-hole Method with Dual-plane Probe Guidance
05:35

Transperineal Prostate Biopsy Using a Cone-shaped Double-hole Method with Dual-plane Probe Guidance

Published on: June 6, 2025

2.6K

Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Personalized Medicine

Background:

  • Personalized medicine necessitates integrated health data beyond isolated streams.
  • Digital twins (DTs) offer individualized biological simulations but face limitations with narrow data sources.
  • Next-generation DTs require multi-scale data integration for accurate health and disease modeling.

Purpose of the Study:

  • To review the conceptual, technical, and clinical aspects of multi-scale digital twins (MSDTs).
  • To explore enabling technologies and applications of MSDTs in dynamic health interventions.
  • To identify persistent challenges in MSDT development and implementation.

Main Methods:

  • Review of conceptual foundations and technical architectures for MSDTs.
  • Synthesis of enabling technologies: multimodal data fusion, graph neural networks, causal inference, reinforcement learning, and hybrid AI-mechanistic models.
  • Analysis of clinical applications and ethical considerations.

Main Results:

  • MSDTs integrate molecular, cellular, tissue, organ, clinical, behavioral, and environmental data.
  • Key technologies facilitate dynamic health trajectory modeling and personalized interventions.
  • Significant barriers include data integration, ethical governance, bias mitigation, and regulatory adaptation.

Conclusions:

  • MSDTs represent a critical advancement for personalized medicine, enabling comprehensive health simulations.
  • Overcoming technical and ethical challenges is crucial for realizing the full clinical potential of MSDTs.
  • Future research should focus on robust data integration and responsible AI implementation for MSDTs.