Evidence map›Paper›PMID 34442389›Full record

ReviewJournal of personalized medicine2021

Digital Twins: From Personalised Medicine to Precision Public Health.

Maged N Kamel Boulos, Peng Zhang

Open access · goldAbstract readReview
In one paragraph

Review in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 156 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
156citing papers in PubMed, 4 pooled it
24.3field-weighted citation impact, top 1% of its field
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

156 citing papers in PubMed, 4 syntheses or guidelines pooled it, 463 citations in OpenAlex.

  1. Pooled it
  2. Digital Twins and Health Care: an Umbrella Review.Journal of medical systems · 2025
    Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Review
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  16. Digital Twin Technology In Radiology.Journal of imaging informatics in medicine · 2026
    Review
  17. Article
  18. Article
  19. Article
  20. Review

96 more citing papers are in PubMed but not listed here.

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

2 authors at 2 institutions in 2 countries.

Maged N Kamel BoulosInformation Management School, Sun Yat-sen University, Guangzhou 510006, China.ORCID 0000-0003-2400-6303
Peng ZhangData Science Institute & Department of Computer Science, Vanderbilt University, Nashville, TN 37240, USA.
Sun Yat-sen University · CNVanderbilt University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A digital twin is a virtual model of a physical entity, with dynamic, bi-directional links between the physical entity and its corresponding twin in the digital domain. Digital twins are increasingly used today in different industry sectors. Applied to medicine and public health, digital twin technology can drive a much-needed radical transformation of traditional electronic health/medical records (focusing on individuals) and their aggregates (covering populations) to make them ready for a new era of precision (and accuracy) medicine and public health. Digital twins enable learning and discovering new knowledge, new hypothesis generation and testing, and

Indexed as

digital twinshuman digital twinspersonalised medicineprecision medicineprecision public health

Identifiers

PMID34442389
PMCPMC8401029
OpenAlexW3184569878

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.