Evidence map›Paper›PMID 41643212›Full record

ArticleJMIR AI2026

Ethical Risks and Structural Implications of AI-Mediated Medical Interpreting.

Alexandra Lopez Vera

Abstract read
In one paragraph

Article in JMIR AI, 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.

Alexandra Lopez VeraCalifornia University of Science and Medicine, 1501 Violet St, Colton, CA, 92324, United States, 1 9095809661.ORCID http://orcid.org/0000-0001-7659-3672

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: Artificial intelligence (AI) is increasingly used to support medical interpreting and public health communication, yet current systems introduce serious risks to accuracy, confidentiality, and equity, particularly for speakers of low-resource languages. Automatic translation models often struggle with regional varieties, figurative language, culturally embedded meanings, and emotionally sensitive conversations about reproductive health or chronic disease, which can lead to clinically significant misunderstandings. These limitations threaten patient safety, informed consent, and trust in health systems when clinicians rely on AI as if it were a professional interpreter. At the same time, the large data sets required to train and maintain these systems create new concerns about surveillance, secondary use of linguistic data, and gaps in existing privacy protections. This viewpoint examines the ethical and structural implications of AI-mediated interpreting in clinical and public health settings, arguing that its routine use as a replacement for qualified interpreters would normalize a lower standard of care for people with Non-English Language Preference and reinforce existing health disparities. Instead, AI tools should be treated as optional, carefully evaluated supplements that operate under the supervision of trained clinicians and professional interpreters, within clear regulatory guardrails for transparency, accountability, and community oversight. The paper concludes that language access must remain grounded in human expertise, language rights, and structural commitments to equity, rather than in cost-saving promises of automated systems.

Indexed as

AI-mediated interpretingartificial intelligenceclinical communicationhealth equitylanguage access

Identifiers

PMID41643212
PMCPMC12875660

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.