Evidence map›Paper›PMID 42527511›Full record

Articlenpj health systems2026

Evaluating LingualAI: a prospective validation of AI-based real-time translation against certified human interpreters.

Uday P Singh, Carlos A Jaimes Garcia, Gabriel M Aisenberg, Javier Barreda Garcia, Jessica A Hernandez-Chilatra, Cecilia Wang, Dalilah Reyes de Jesus, Eileen Whalen, Veronica Santos Canellas, Amanda R Falk Vargas and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in npj health systems, 2026. 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

14 authors.

Uday P SinghMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA. uday.pratapsingh@uth.tmc.edu.
Carlos A Jaimes GarciaMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Gabriel M AisenbergMcGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
Javier Barreda GarciaMcGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
Jessica A Hernandez-ChilatraMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Cecilia WangMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Dalilah Reyes de JesusMcGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
Eileen WhalenMcGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
Veronica Santos CanellasMcGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
Amanda R Falk VargasMcGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
Brian O Rodriguez EchevarriaMcGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
Martin J CitardiMcGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
Babatope O FatuyiMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Xiaoqian JiangMcWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA. xiaoqian.Jiang@uth.tmc.edu.

Funding

University of Texas Health Science Center at Houston Institutional Funds
6 · The paper itself

Abstract

Limited English proficiency affects over 25 million people in the United States and is associated with disparities in healthcare access, safety, and outcomes. We conducted a prospective, within-subject, simulation-based comparison to evaluate whether an in-house AI application (LingualAI) achieves non-inferior translation quality versus certified medical interpreters in English-Spanish otorhinolaryngology encounters. Standardized clinician-patient scripts were translated by LingualAI and by certified interpreters, and bilingual clinicians rated anonymized audio across multidomain quality measures. Using a prespecified non-inferiority margin of 0.30 points (Human - AI) on 5-point scales, LingualAI met non-inferiority for 2 of 3 primary factors (terminology accuracy Δ = 0.07; adequacy of meaning Δ = 0.13) but not clarity (Δ = 0.50). It met non-inferiority for 1 secondary factor (completeness Δ = 0.14), while grammar (Δ = 0.21; upper 95% CI = 0.34), vocabulary (Δ = 0.18; upper 95% CI = 0.32), and cultural appropriateness (Δ = 0.39) exceeded the margin. No voice-related factors met non-inferiority (fluency Δ = 1.13; prosody Δ = 0.59; pacing Δ = 0.40), and conclusive ratings favored interpreters (overall quality Δ = 0.58; clinical confidence Δ = 0.61). These findings suggest LingualAI preserves core clinical meaning and terminology but remains limited by speech naturalness and delivery, supporting use as an adjunct when interpreter access is constrained and favoring interpreter-in-the-loop deployment for higher-stakes communication.

Identifiers

PMID42527511
PMCPMC13354185

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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.