Evidence map›Paper›PMID 41533280›Full record

ArticleCJEM2026

ChatGPT-guided translation of pediatric emergency department discharge instructions.

Andrew Yuanbo Wang, Kristine Van Aarsen, Sheena Belisle

Abstract read
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In one paragraph

Article in CJEM, 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

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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

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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

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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

3 authors.

Andrew Yuanbo WangSchulich School of Medicine and Dentistry, Western University, London, ON, Canada.
Kristine Van AarsenLawson Health Research Institute, London Health Sciences Centre, London, ON, Canada.
Sheena BelisleDivision of Paediatric Emergency Medicine, Department of Paediatrics, Schulich School of Medicine & Dentistry, University of Western Ontario, London, ON, Canada. sheena.belisle@lhsc.on.ca.ORCID 0009-0005-1545-1431

Funding

Paediatric Emergency Medicine Associates
6 · The paper itself

Abstract

backgroundLanguage barriers in pediatric emergency medicine discharge instructions can impact patient safety, leading to poorer post-discharge outcomes. Google Translate has been widely used clinically as an alternative to professional translations but is prone to errors. ChatGPT-4 is a large language model that has been shown to offer improved translation abilities in various domains. This study compares ChatGPT-4 and Google Translate for translating discharge instructions into Arabic and Simplified Chinese.

methods15 standardized pediatric emergency department discharge instructions from our institution were translated from English to both Simplified Chinese and Arabic, using both ChatGPT-4 and Google Translate. Eight bilingual medical professionals (four per language group) were recruited as participants and presented with the English and the translated discharge instructions in a blinded manner. Participants rated the translations on a 5-point Likert scale for accuracy, safety, and understandability, followed by their overall translation preference.

resultsIn Arabic, ChatGPT-4 was found to be more accurate than Google Translate, (mean 4.1 vs. 3.7, mean difference = 0.43, 95% CI 0.15, 0.72, p = 0.003). Similarly for Simplified Chinese, ChatGPT-4 was found to be more accurate than Google Translate (mean 4.4 vs. 4.2; mean difference = 0.28, 95% CI 0.07, 0.50, p = 0.010). There were no significant differences in safety or understandability between ChatGPT-4 and Google Translate in either language. ChatGPT-4-generated translations were consistently preferred over Google Translate in both languages.

conclusionOverall, ChatGPT-4 is a more accurate alternative to Google Translate in translating written pediatric discharge instructions from English to Simplified Chinese and Arabic. ChatGPT-4 could be a particularly valuable translation tool in healthcare settings with limited access to translation resources such as professional interpreters.

Indexed as

Emergency Service, HospitalPatient DischargeTranslatingChildCommunication BarriersFemaleHumansLarge Language ModelsMaleArtificial intelligenceDischarge instructionsTranslation

Identifiers

PMID41533280

What OpenQuestion holds

Textmetadata
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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.