Evidence map›Paper›PMID 39590593›Full record

ReviewJournal of personalized medicine2024

Digital Twins' Advancements and Applications in Healthcare, Towards Precision Medicine.

Konstantinos Papachristou, Paraskevi F Katsakiori, Panagiotis Papadimitroulas, Lidia Strigari, George C Kagadis

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of personalized medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 2 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

38 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Review
  5. Review
  6. Review
  7. Article
  8. Review
  9. Review
  10. Review
  11. Article
  12. Review
  13. Article
  14. Article
  15. Artificial intelligence in preventive care in primary health care settings: a scoping review.Archives of medical sciences. Atherosclerotic diseases · 2026
    Article
  16. Article
  17. Review
  18. Review
  19. Review
  20. Review
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

5 authors.

Konstantinos Papachristou3dmi Research Group, Department of Medical Physics, School of Medicine, University of Patras, 26504 Rion, Greece.
Paraskevi F Katsakiori3dmi Research Group, Department of Medical Physics, School of Medicine, University of Patras, 26504 Rion, Greece.
Panagiotis Papadimitroulas3dmi Research Group, Department of Medical Physics, School of Medicine, University of Patras, 26504 Rion, Greece.ORCID 0000-0002-5981-6149
Lidia StrigariDepartment of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy.ORCID 0000-0003-4293-2298
George C Kagadis3dmi Research Group, Department of Medical Physics, School of Medicine, University of Patras, 26504 Rion, Greece.ORCID 0000-0002-6983-2863

Funding

Hellenic Foundation for Research and Innovation 2692Marie Skłodowska-Curie 872735
6 · The paper itself

Abstract

This review examines the significant influence of Digital Twins (DTs) and their variant, Digital Human Twins (DHTs), on the healthcare field. DTs represent virtual replicas that encapsulate both medical and physiological characteristics-such as tissues, organs, and biokinetic data-of patients. These virtual models facilitate a deeper understanding of disease progression and enhance the customization and optimization of treatment plans by modeling complex interactions between genetic factors and environmental influences. By establishing dynamic, bidirectional connections between the DTs of physical objects and their digital counterparts, these technologies enable real-time data exchange, thereby transforming electronic health records. Leveraging the increasing availability of extensive historical datasets from clinical trials and real-world sources, AI models can now generate comprehensive predictions of future health outcomes for specific patients in the form of AI-generated DTs. Such models can also offer insights into potential diagnoses, disease progression, and treatment responses. This remarkable progression in healthcare paves the way for precision medicine and personalized health, allowing for high-level individualized medical interventions and therapies. However, the integration of DTs into healthcare faces several challenges, including data security, accessibility, bias, and quality. Addressing these obstacles is crucial to realizing the full potential of DHTs, heralding a new era of personalized, precise, and accurate medicine.

Indexed as

artificial intelligencedigital twinshealthcarepersonalized medicineprecision medicine

Identifiers

PMID39590593
PMCPMC11595921

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.