Evidence map›Paper›PMID 41174931›Full record

ArticleBioethics2026

Digitizing Dignity: Analyzing Digital Twins Through the Lens of Multidimensional Human Dignity.

Andrew J Barnhart

Abstract read
In one paragraph

Article in Bioethics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

1 author.

Andrew J BarnhartDepartment of Social Ethics, University of Bonn, Bonn, North Rhine-Westphalia, Germany.ORCID 0000-0002-7935-253X

Funding

This study has been funded/co-funded by the European Research Council as part of ERC Starting Grant SIMTWIN (No. 101076822). Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.
6 · The paper itself

Abstract

In precision medicine, digital twins-virtual models of patients created using personalized data and advanced machine learning-are potentially changing healthcare by predicting health outcomes and guiding medical decisions. However, their use raises complex ethical questions, particularly concerning their relationship to human dignity. Patients often regard dignity as central to their healthcare experience, and failing to incorporate this principle into the design and application of digital twins risks undermining personal autonomy, misusing sensitive data, eroding patient-provider trust, and creating broader ethical challenges. This paper argues that digital twins are not mere data sets or predictive tools, but symbolic extensions of the dignity of the individuals they represent. Using David Kirchhoffer's multidimensional framework of human dignity, this study examines how digital twins engage with both absolute (inherent) and contingent (socially constructed) dimensions of dignity. The analysis begins by exploring the multidimensional concept of human dignity, followed by a discussion of how digital twins embody these dimensions, illustrated through examples such as digital brain twins, posthumous digital representations, and disability contexts. Finally, the paper addresses the ethical implications of these findings, emphasizing the moral responsibilities of researchers, developers, and clinicians to treat digital twins as representations of patient dignity, thereby ensuring these technologies advance healthcare without compromising the fundamental respect owed to every individual.

Indexed as

PersonhoodPrecision MedicineRespectHumansMachine LearningPersonal AutonomyTrustartificial intelligencebioethicsdigital twinshuman dignitypersonalized medicine

Identifiers

PMID41174931
PMCPMC13179012

What OpenQuestion holds

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Registered trials

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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.