Evidence map›Paper›PMID 41635730›Full record

ArticleAI and ethics2026

Machine learning for precision medicine: promoting value considerations through perspective-taking hypothetical group design exercises.

Tehmi E den Braven, Ariadne A Nichol, Matthew D Kearney, Mildred K Cho, Pamela L Sankar

Abstract read
In one paragraph

Article in AI and ethics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Tehmi E den Braven *Center for Biomedical Ethics, Stanford University School of Medicine, Stanford, USA.ORCID 0009-0006-4851-2429
Ariadne A Nichol *Center for Biomedical Ethics, Stanford University School of Medicine, Stanford, USA.ORCID 0000-0001-9174-9671
Matthew D KearneyDepartment of Family Medicine and Community Health, Perelman School of Medicine, University of Pennsylvania , Philadelphia, USA.ORCID 0000-0003-2898-573X
Mildred K ChoCenter for Biomedical Ethics, Stanford University School of Medicine, Stanford, USA.ORCID 0000-0003-1669-3932
Pamela L SankarDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.ORCID 0000-0002-7780-9655

Funding

Integrating Ethics into Machine Learning for Precision MedicineR01HG010476 · NHGRI · STANFORD UNIVERSITY · PI CHO, MILDRED K., SANKAR, PAMELA L · 2019 to 2021
$1.8M
NHGRI NIH HHS R01 HG010476
6 · The paper itself

Abstract

Public concerns over the social and ethical consequences of artificial intelligence (AI) are well established. Despite ongoing efforts to respond, these concerns remain largely unresolved by either regulation or codes of ethics. In response, scholars have advanced ideas about how to better ground ethical awareness in practice. A key element of this grounding is fostering awareness of how one's actions can affect the welfare of others. We tested the effect of a group design exercise on whether and how AI developers considered the impact of their work on others, using perspective-taking as a "values lever"-a practice that prompts ethical reflection during the design process. We found that hypothetical scenarios set in three different contexts of AI research or building a tool for clinical use encouraged developers to take different perspectives. We specifically used an Supplementary Information: The online version contains supplementary material available at 10.1007/s43681-025-00973-5.

Indexed as

DevelopersEthicsMachine learningPrecision medicineResponsibility

Identifiers

PMID41635730
PMCPMC12862023

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.