Evidence map›Paper›PMID 39920778›Full record

ReviewGenome medicine2025

Digital twins as global learning health and disease models for preventive and personalized medicine.

Xinxiu Li, Joseph Loscalzo, A K M Firoj Mahmud, Dina Mansour Aly, Andrey Rzhetsky, Marinka Zitnik, Mikael Benson

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Genome medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

48 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. Review
  6. Review
  7. Review
  8. Article
  9. Review
  10. Article
  11. Review
  12. Review
  13. Review
  14. Article
  15. Review
  16. Article
  17. Review
  18. Review
  19. Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Xinxiu LiMedical Digital Twin Research Group, Department of Clinical Sciences Intervention and Technology, Karolinska Institute, Stockholm, Sweden.
Joseph LoscalzoBrigham and Women's Hospital, Harvard Medical School, Boston, USA.
A K M Firoj MahmudDepartment of Medical Biochemistry and Microbiology, Uppsala University, 75105, Uppsala, Sweden.
Dina Mansour AlyMedical Digital Twin Research Group, Department of Clinical Sciences Intervention and Technology, Karolinska Institute, Stockholm, Sweden.
Andrey RzhetskyDepartments of Medicine and Human Genetics, Institute for Genomics and Systems Biology, University of Chicago, Chicago, USA.
Marinka ZitnikDepartment of Biomedical Informatics, Harvard Medical School, Cambridge, MA, USA.
Mikael BensonMedical Digital Twin Research Group, Department of Clinical Sciences Intervention and Technology, Karolinska Institute, Stockholm, Sweden. mikael.benson@ki.se.ORCID 0000-0002-7753-9181

Funding

Branched-chain Keto-acids and Aerobic Glycolysis in Vascular Smooth Muscle CellsR01HL166137 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI Joseph Loscalzo · 2023 to 2026
$2.8M
5/5 Clinical Outcome Prediction of Psychosis from Electronic Health Records (COPPER)R01MH137646 · NIMH · UNIVERSITY OF CHICAGO · PI ANDREY RZHETSKY · 2024 to 2026
$779k
American Heart Association AHA957729American Heart Association AHAMERIT1185447EU HorizonHealth 2021 101057619NIMH NIH HHS R01 MH137646NINR NIH HHS R01 HL1551107NINR NIH HHS R01 HL166137NINR NIH HHS U01 HG007691
6 · The paper itself

Abstract

Ineffective medication is a major healthcare problem causing significant patient suffering and economic costs. This issue stems from the complex nature of diseases, which involve altered interactions among thousands of genes across multiple cell types and organs. Disease progression can vary between patients and over time, influenced by genetic and environmental factors. To address this challenge, digital twins have emerged as a promising approach, which have led to international initiatives aiming at clinical implementations. Digital twins are virtual representations of health and disease processes that can integrate real-time data and simulations to predict, prevent, and personalize treatments. Early clinical applications of DTs have shown potential in areas like artificial organs, cancer, cardiology, and hospital workflow optimization. However, widespread implementation faces several challenges: (1) characterizing dynamic molecular changes across multiple biological scales; (2) developing computational methods to integrate data into DTs; (3) prioritizing disease mechanisms and therapeutic targets; (4) creating interoperable DT systems that can learn from each other; (5) designing user-friendly interfaces for patients and clinicians; (6) scaling DT technology globally for equitable healthcare access; (7) addressing ethical, regulatory, and financial considerations. Overcoming these hurdles could pave the way for more predictive, preventive, and personalized medicine, potentially transforming healthcare delivery and improving patient outcomes.

Indexed as

Patient-Specific ModelingPrecision MedicineHumansInappropriate PrescribingPreventive MedicineTreatment FailureComputational methodsData integrationDigital twinsPersonalized medicine

Identifiers

PMID39920778
PMCPMC11806862

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.