Digital Phantoms in Medical Research : Synthetic Data and the Pursuit of Ground Truth
Högberg, Charlotte LU
and Winter, Peter
(2026)
In Big Data and Society
13(2).
p.1-12
- Abstract
- Digital phantoms are virtual representations of the human body used in medical research to test equipment, train medical professionals and develop or validate algorithms. These models can be created from ‘real-world’ clinical data or from ‘synthetic data’. Phantoms derived from clinical data often serves as ‘ground truth’ reference values anchored in empirical observations. However, there is growing demand for synthetic digital phantoms and datasets that do not originate from real patients, raising critical questions about how reliable knowledge is produced from data detached from reality. This article aims to investigate these issues through a document analysis of peer-reviewed publications on the development and use of digital phantoms... (More)
- Digital phantoms are virtual representations of the human body used in medical research to test equipment, train medical professionals and develop or validate algorithms. These models can be created from ‘real-world’ clinical data or from ‘synthetic data’. Phantoms derived from clinical data often serves as ‘ground truth’ reference values anchored in empirical observations. However, there is growing demand for synthetic digital phantoms and datasets that do not originate from real patients, raising critical questions about how reliable knowledge is produced from data detached from reality. This article aims to investigate these issues through a document analysis of peer-reviewed publications on the development and use of digital phantoms in medical physics. We examine how researchers construct ‘ground truth’ and the challenges they encounter when advancing truth claims through technical work. By attending to the bodies fabricated in phantom creation and to the data made to represent human form, we show how synthetic data – detached from real human subjects – are valued for enabling researchers to sidestep the complexities or ‘messiness’ of real-world patients and clinical data. Moreover, we show how synthetic phantoms and data are framed as tools that enhance control and flexibility, functioning as ‘known truths’: workable approximations that enables the construction of what are claimed to be more representative datasets and models. This article contributes to STS and critical data studies by examining the nature and implications of digital representations and synthetic data in the development of machine-learning models in medicine, and the truth claims they support. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5bd0a0be-771c-44c1-a033-847e6bd9ad41
- author
- Högberg, Charlotte
LU
and Winter, Peter
- organization
- publishing date
- 2026-05-04
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Digital Phantoms, synthetic data, Artifical Intelligence, Ground truth, Simulation, Medical research
- in
- Big Data and Society
- volume
- 13
- issue
- 2
- pages
- 12 pages
- publisher
- SAGE Publications
- ISSN
- 2053-9517
- DOI
- 10.1177/20539517261447840
- language
- English
- LU publication?
- yes
- id
- 5bd0a0be-771c-44c1-a033-847e6bd9ad41
- date added to LUP
- 2026-03-27 13:37:31
- date last changed
- 2026-05-19 09:37:33
@article{5bd0a0be-771c-44c1-a033-847e6bd9ad41,
abstract = {{Digital phantoms are virtual representations of the human body used in medical research to test equipment, train medical professionals and develop or validate algorithms. These models can be created from ‘real-world’ clinical data or from ‘synthetic data’. Phantoms derived from clinical data often serves as ‘ground truth’ reference values anchored in empirical observations. However, there is growing demand for synthetic digital phantoms and datasets that do not originate from real patients, raising critical questions about how reliable knowledge is produced from data detached from reality. This article aims to investigate these issues through a document analysis of peer-reviewed publications on the development and use of digital phantoms in medical physics. We examine how researchers construct ‘ground truth’ and the challenges they encounter when advancing truth claims through technical work. By attending to the bodies fabricated in phantom creation and to the data made to represent human form, we show how synthetic data – detached from real human subjects – are valued for enabling researchers to sidestep the complexities or ‘messiness’ of real-world patients and clinical data. Moreover, we show how synthetic phantoms and data are framed as tools that enhance control and flexibility, functioning as ‘known truths’: workable approximations that enables the construction of what are claimed to be more representative datasets and models. This article contributes to STS and critical data studies by examining the nature and implications of digital representations and synthetic data in the development of machine-learning models in medicine, and the truth claims they support.}},
author = {{Högberg, Charlotte and Winter, Peter}},
issn = {{2053-9517}},
keywords = {{Digital Phantoms; synthetic data; Artifical Intelligence; Ground truth; Simulation; Medical research}},
language = {{eng}},
month = {{05}},
number = {{2}},
pages = {{1--12}},
publisher = {{SAGE Publications}},
series = {{Big Data and Society}},
title = {{Digital Phantoms in Medical Research : Synthetic Data and the Pursuit of Ground Truth}},
url = {{http://dx.doi.org/10.1177/20539517261447840}},
doi = {{10.1177/20539517261447840}},
volume = {{13}},
year = {{2026}},
}