Evidence map›Paper›PMID 40629322›Full record

ArticleBMC medical education2025

A systematic multimodal assessment of AI machine translation tools for enhancing access to critical care education internationally.

Christine L Chen, Yue Dong, Claudia Castillo-Zambrano, Hassan Bencheqroun, Amelia Barwise, Adria Hoffman, Keivan Nalaie, Yishu Qiu, Oualid Boulekbache, Alexander S Niven

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Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. 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

10 authors.

Christine L ChenDivision of Internal Medicine, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA. Chen.Christine@mayo.edu.
Yue DongDivision of Pulmonary and Critical Care Medicine, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA.
Claudia Castillo-ZambranoDivision of Internal Medicine, Dartmouth Hitchcock Medical Center, 1 Medical Center Dr, Lebanon, NH, 03766, USA.
Hassan BencheqrounDivision of Pulmonary and Critical Care Medicine, University of California Riverside School of Medicine, 92521 Botanic Gardens Dr, Riverside, CA, 92507, USA.
Amelia BarwiseDivision of Pulmonary and Critical Care Medicine, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA.
Adria HoffmanSchool of Continuous Professional Development, Mayo Clinic, 200 First St. SW, Rochesster, MN, 55905, USA.
Keivan NalaieDivision of Anesthesiology, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA.
Yishu QiuDes Moines University Medicine and Health Sciences, 8025 Grand Avenue in West Des Moines, Iowa, 50266, USA.
Oualid BoulekbacheDepartment of Language Services, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA.
Alexander S NivenDivision of Pulmonary and Critical Care Medicine, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLanguage barriers pose a significant barrier to expanding access to critical care education worldwide. Machine translation (MT) offers significant promise to increase accessibility to critical care content, and has rapidly evolved using newer artificial intelligence frameworks and large language models. The best approach to systematically apply and evaluate these tools, however, remains unclear.

methodsWe developed a multimodal method to evaluate translations of critical care content used as part of an established international critical care education program. Four freely-available MT tools were selected (DeepL™, Google Gemini™, Google Translate™, Microsoft CoPilot™) and used to translate selected phrases and paragraphs into Chinese (Mandarin), Spanish, and Ukrainian. A human translation performed by a professional medical translator was used for comparison. These translations were compared using 1) blinded bilingual clinician evaluations using anchored Likert domains of fluency, adequacy, and meaning; 2) automated BiLingual Evaluation Understudy (BLEU) scores; and 3) validated system usability scale to assess the ease of use of MT tools. Blinded bilingual clinician evaluations were calculated as individual domains and averaged composite scores.

resultsBlinded clinician composite scores were highest for human translation (Chinese), Google Gemini (Spanish), and Microsoft CoPilot (Ukrainian). Microsoft CoPilot (Chinese) and Google Translate (Spanish and Ukrainian) earned the lowest scores. All Chinese and Spanish versions received "understandable to good" or "high quality" BLEU scores, while Ukrainian overall scored "hard to get the gist" except using Microsoft CoPilot. Usability scores were highest with DeepL (Chinese), Google Gemini (Spanish), and Google Translate (Ukrainian), and lower with Microsoft CoPilot (Chinese and Ukrainian) and Google Translate (Spanish).

conclusionNo single MT tool performed best across all metrics and languages, highlighting the importance of routine assessment of these tools during educational activities given their rapid ongoing evolution. We offer a multimodal evaluation methodology to aid this assessment as medical educators expand their use of MT in international educational programs.

Indexed as

Artificial IntelligenceCritical CareTranslatingTranslationsCommunication BarriersHumansInternationalityLanguageMultilingualismArtificial intelligenceCritical care educationLanguage translationMachine translation

Identifiers

PMID40629322
PMCPMC12239318

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.