Evidence map›Paper›PMID 35992534›Full record

ReviewJAMIA open2022

Digital twins to enable better precision and personalized dementia care.

Nilmini Wickramasinghe, Nalika Ulapane, Amir Andargoli, Chinedu Ossai, Nadeem Shuakat, Tuan Nguyen, John Zelcer

Abstract readReview
In one paragraph

Review in JAMIA open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Review
  7. Utilization of precision medicine digital twins for drug discovery in Alzheimer's disease.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025
    Review
  8. Review
  9. Review
  10. Opportunities for use of neuroimaging in de-risking drug development and improving clinical outcomes in psychiatry: an industry perspective.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024
    Review
  11. Digital Twins in Neuroscience.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024
    Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Intelligent Digital Twins for Personalized Migraine Care.Journal of personalized medicine · 2023
    Review
  18. Article
  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

7 authors.

Nilmini WickramasingheSwinburne University of Technology, School of Health Sciences, Department of Health and Bio Statistics, Hawthorn, Australia.ORCID https://orcid.org/0000-0002-1314-8843
Nalika UlapaneSwinburne University of Technology, School of Health Sciences, Department of Health and Bio Statistics, Hawthorn, Australia.
Amir AndargoliSwinburne University of Technology, School of Health Sciences, Department of Health and Bio Statistics, Hawthorn, Australia.
Chinedu OssaiSwinburne University of Technology, School of Health Sciences, Department of Health and Bio Statistics, Hawthorn, Australia.
Nadeem ShuakatSwinburne University of Technology, School of Health Sciences, Department of Health and Bio Statistics, Hawthorn, Australia.
Tuan NguyenSwinburne University of Technology, School of Health Sciences, Department of Health and Bio Statistics, Hawthorn, Australia.
John ZelcerSwinburne University of Technology, School of Health Sciences, Department of Health and Bio Statistics, Hawthorn, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this perspective paper, we want to highlight the potential benefits of incorporating digital twins to support better dementia care. In particular, we assert that, by doing so, it is possible to ensure greater precision regarding dementia care while simultaneously enhancing personalization. Digital twins have been used successfully in manufacturing to enable better prediction and tailoring of solutions to meet required needs, and thereby have enabled more effective and efficient deployment of resources. We develop a model for digital twin in the healthcare domain as a clinical decision support tool by extrapolating its current uses from the manufacturing domain. We illustrate the power of the developed model in the context of dementia. Given the rapid rise of chronic conditions and the pressures on healthcare delivery to provide high quality, cost-effective care anywhere and anytime, we assert that such an approach is consistent with a value-based healthcare philosophy and thus important as the numbers of people with dementia continues to grow exponentially and this pressing healthcare issue is yet to be optimally addressed. Further research and development in this rapidly evolving domain is a strategic priority for ensuring the delivery of superior dementia care.

Indexed as

artificial intelligenceclinical decision supportdementiadigital twinmachine learning

Identifiers

PMID35992534
PMCPMC9387506

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.