Evidence map›Paper›PMID 36138125›Full record

ArticleNPJ digital medicine2022

Health digital twins as tools for precision medicine: Considerations for computation, implementation, and regulation.

Kaushik P Venkatesh, Marium M Raza, Joseph C Kvedar

Abstract readEditorial
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
102citing 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

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

  1. Pooled it
  2. Review
  3. AI-enabled clinical trials.Nature reviews bioengineering · 2026
    Article
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  5. Artificial intelligence virtual bone organoids (AIVBOs).Journal of orthopaedic translation · 2026
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42 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

3 authors.

Kaushik P VenkateshHarvard Medical School, Boston, MA, USA. kaushik_venkatesh@hms.harvard.edu.ORCID http://orcid.org/0000-0002-1736-4367
Marium M RazaHarvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0001-8050-9402
Joseph C KvedarHarvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0002-7517-2291

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Health digital twins are defined as virtual representations ("digital twin") of patients ("physical twin") that are generated from multimodal patient data, population data, and real-time updates on patient and environmental variables. With appropriate use, HDTs can model random perturbations on the digital twin to gain insight into the expected behavior of the physical twin-offering groundbreaking applications in precision medicine, clinical trials, and public health. Main considerations for translating HDT research into clinical practice include computational requirements, clinical implementation, as well as data governance, and product oversight.

Identifiers

PMID36138125
PMCPMC9500019

What OpenQuestion holds

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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.