Evidence map›Paper›PMID 40374901›Full record

ReviewNPJ digital medicine2025

Digital twins for the era of personalized surgery.

Yosra Magdi Mekki, Gijs Luijten, Elisabet Hagert, Sirajeddin Belkhair, Chris Varghese, Junaid Qadir, Barry Solaiman, Muhammad Bilal, Jaghtar Dhanda, Jan Egger and 5 more

Abstract readReview
In one paragraph

Review in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
–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

26 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Anatomy-resolved digital twin framework for personalized light dosimetry in lung therapies.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  5. Review
  6. Colour-Guided Surgery: A Conceptual Framework for Multimodal Intraoperative Visualisation-A Narrative Review.The international journal of medical robotics + computer assisted surgery : MRCAS · 2026
    Review
  7. Article
  8. Review
  9. Review
  10. Perception-first digital twin for augmented reality microsurgery.International journal of computer assisted radiology and surgery · 2026
    Article
  11. Review
  12. Review
  13. Review
  14. Review
  15. Article
  16. Review
  17. Review
  18. Current Uses for Medical Imaging With Orthopaedic Implant Technology.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2026
    Review
  19. Article
  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

15 authors.

Yosra Magdi MekkiCollege of Medicine, Qatar University, QU Health, Doha, Qatar. yusramagdi@gmail.com.
Gijs LuijtenInstitute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital (AöR), Essen, Germany.
Elisabet HagertCollege of Medicine, Qatar University, QU Health, Doha, Qatar.
Sirajeddin BelkhairCollege of Medicine, Qatar University, QU Health, Doha, Qatar.
Chris VargheseDepartment of Surgery, University of Auckland, Auckland, New Zealand.
Junaid QadirCollege of Engineering, Qatar University, Doha, Qatar.
Barry SolaimanWeill Cornell Medicine - Qatar, Doha, Qatar.
Muhammad BilalFaculty of Business, Law and Social Sciences, Birmingham City University, Birmingham, UK.
Jaghtar DhandaHead & Neck Unit, Queen Victoria Hospital NHS Foundation Trust, Grinstead, West Sussex, UK.
Jan EggerInstitute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital (AöR), Essen, Germany.
Jun DengDepartment of Therapeutic Radiology, Yale University, New Haven, CT, USA.
Vikas KhandujaAddenbrooke's-Cambridge University Hospitals NHS Trust, Cambridge, UK.
Alejandro F FrangiCentre for Computational Imaging and Modelling in Medicine (CIMIM), University of Manchester, Manchester, UK.
Susu M ZughaierCollege of Medicine, Qatar University, QU Health, Doha, Qatar.
Mitchell A StotlandWeill Cornell Medicine - Qatar, Doha, Qatar.

Funding

Qatar University QU HIG 23/24-127
6 · The paper itself

Abstract

Digital twins can aid surgeons in training and in performing interventions with greater awareness and precision. The range and variety of digital twins in surgery are described, and their use across perioperative care is discussed. While largely experimental, they are beginning to show promise for the enhancement of personalized, adaptive, and data-driven surgical care. Issues relevant to the greater adoption and deployment of digital twins are all considered.

Identifiers

PMID40374901
PMCPMC12081715

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.