Evidence map›Paper›PMID 42339615›Full record

ArticleWomen's health (London, England)

Beyond gender and racial bias: Towards pro-justice ethical GenAI use in medicine and health.

Soumyadeep Bhaumik

Abstract readEditorial
In one paragraph

Article in Women's health (London, England). 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

1 author.

Soumyadeep BhaumikMeta-research and Evidence Synthesis Unit, The George Institute for Global Health, Sydney, Australia.ORCID 0000-0001-9579-4453

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generative artificial intelligence(GenAI) is increasingly being used in the health sector. Much of the current discourse on GenAI is focused on technical accuracy and bias in its outputs. This editorial argues the need to go beyond bias mitigation and take a pro-justice approach for ethical GenAI use in health and medicine: one that actively confronts injustices, addresses structural inequities, and considers our intrinsic intergenerational responsibilities towards nature, humans everywhere , and other species. The piece draws from feminist ethics, justice theories, and decolonial practices to outline four key principles for a pro-justice approach for ethical GenAI use in medicine and health: intersectionality, epistemic justice, environmental justice, and social justice. It highlights the importance of such an approach, particularly for women's health.

Indexed as

Generative Artificial IntelligenceRacismSocial JusticeFemaleFeminismHumansSexismWomen's Healthenvironmental justiceethicsgenerative artificial intelligencegovernancepolicysocial justicewomen’s health

Identifiers

PMID42339615
PMCPMC13305507

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.