Evidence map›Paper›PMID 40549671›Full record

ArticlePLoS biology2025

AI-mediated translation presents two possible futures for academic publishing in a multilingual world.

Tatsuya Amano, Lynne Bowker, Andrew Burton-Jones

Abstract read
In one paragraph

Article in PLoS biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. AI in transplantation: levelling the playing field and raising the methodological standards.Transplant international : official journal of the European Society for Organ Transplantation · 2026
    Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Tatsuya AmanoSchool of the Environment, The University of Queensland, Brisbane, Queensland, Australia.ORCID 0000-0001-6576-3410
Lynne BowkerDépartement de langues, linguistique et traduction, Université Laval, Québec City, Québec, Canada.
Andrew Burton-JonesSchool of Business, The University of Queensland, Brisbane, Queensland, Australia.

Funding

Australian Research CouncilSocial Sciences and Humanities Research Council of CanadaThe University of Queensland
6 · The paper itself

Abstract

As the availability and performance of artificial intelligence for language editing and translation continues to improve, we can imagine a future in which everyone can use their own language to write, assess, and read science. The question is, how can we achieve it?

Indexed as

Artificial IntelligenceMultilingualismPublishingTranslatingTranslationsHumansLanguage

Identifiers

PMID40549671
PMCPMC12185007

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.