Evidence map›Paper›PMID 41136708›Full record

ArticleNPJ digital medicine2025

Evaluating human-in-the-loop strategies for artificial intelligence-enabled translation of patient discharge instructions: a multidisciplinary analysis.

Ryan Cl Brewster, Gabriel Tse, Angela L Fan, Marwa Elborki, Maiah Newell, Priscilla Gonzalez, Amitra Hoq, Crystal Chang, Maksud Chowdhury, Adiba Geeti and 19 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Disparate language and model effects on AI-based translation and recognition of genetic conditions.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  5. Article
  6. Article
  7. Review
  8. Review
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

29 authors.

Ryan Cl BrewsterDivision of General Pediatrics, Boston Children's Hospital, Boston, MA, USA. ryan.brewster@childrens.harvard.edu.
Gabriel TseDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
Angela L FanDivision of General Pediatrics, Boston Children's Hospital, Boston, MA, USA.
Marwa ElborkiDivision of General Pediatrics, Boston Children's Hospital, Boston, MA, USA.
Maiah NewellDivision of General Pediatrics, Boston Children's Hospital, Boston, MA, USA.
Priscilla GonzalezDivision of General Pediatrics, Boston Children's Hospital, Boston, MA, USA.
Amitra HoqNew York University College of Global Public Health, New York City, NY, USA.
Crystal ChangKaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA, USA.
Maksud ChowdhuryDepartment of Pediatrics, State University of New York Downstate Medical Center, Brooklyn, NY, USA.
Adiba GeetiDivision of Pulmonary Medicine, New York University Langone Grossman School of Medicine, New York City, NY, USA.
Marlin HanaDepartment of Allergy and Immunology, Children's Hospital of Richmond at Virginia Commonwealth University, Richmond, VA, USA.
Hoda HassanDepartment of Psychiatry and Behavioral Health, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Osama IbrahimDivision of General Pediatrics, Boston Children's Hospital, Boston, MA, USA.
Lucine KeseyanDepartment of Hematology and Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Qing LiSchool of Nursing, University of Mississippi Medical Center, Jackson, MS, USA.
Md MamoonDepartment of Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
Maymona NageyeAvalon University School of Medicine, Willemstad, CW, USA.
Arthur OhannessianDepartment of Family Medicine, University of California Los Angeles School of Medicine, Los Angeles, CA, USA.
Ilan Rozen EisenbergDivision of Pediatric Infectious Diseases, Department of Pediatrics, Boston Medical Center, Boston, MA, USA.
Mohammad SallamDepartment of Pediatric Critical Care, MercyOne Des Moines Medical Center, Des Moines, IA, USA.
Giordano Sosa SotoDepartment of Pediatrics, Harvard Medical School, Boston, MA, USA.
Christina SuDivision of General Pediatrics, Boston Children's Hospital, Boston, MA, USA.
Raffi TachdjianDivision of Allergy, Immunology and Rheumatology, University of California Los Angeles School of Medicine, Los Angeles, CA, USA.
Mondira RayDivision of General Pediatrics, Boston Children's Hospital, Boston, MA, USA.
Hannah LevBiostatistics and Research Design (BARD) Center, Boston Children's Hospital, Boston, MA, USA.
Jonathan D HronDivision of General Pediatrics, Boston Children's Hospital, Boston, MA, USA.
Nate ShaarInterpreter Services, Boston Children's Hospital, Boston, MA, USA.
Nicholas Kuzma *Division of Hospital Medicine, Department of Pediatrics, St. Christopher's Hospital for Children, Drexel University College of Medicine, Philadelphia, PA, USA.
Alisa Khan *Division of General Pediatrics, Boston Children's Hospital, Boston, MA, USA.

Funding

Patient-Centered Outcomes Research Institute AD-2021C3-24848
6 · The paper itself

Abstract

Machine translation supported by artificial intelligence (AI) may enhance linguistically-concordant care for patients speaking languages other than English. This assessment of free-text inpatient discharge instructions in Arabic, Armenian, Bengali, simplified Chinese, Somali, and Spanish compared linguist, clinician, and family caregiver evaluations of translations generated by (1) ChatGPT-4o, (2) professional linguists, and (3) human-in-the-loop (AI-generated, professional linguist post-edited). Likert scales (1-5; higher is better) evaluated linguistic and clinical characteristics of each translation. ChatGPT-4o exhibited variable performance relative to professional translations, with poorest ratings for digitally underrepresented languages (Armenian and Somali). Conversely, human-in-the-loop translations achieved comparable, often better, outcomes to professional translations for all languages, (e.g., Armenian mean overall quality: 3.9 [95% CI 3.7-4.2] vs. professional 3.6 [3.4-3.9], p = 0.01), were most frequently preferred (46.5% vs. 28.4%) and had shorter mean translation time (7.1 [5.4-8.8] vs. 16.8 [13.7-19.9] min, p < 0.001). Human-in-the-loop strategies may enable safe, efficient, equitable machine translation application in clinical practice.

Identifiers

PMID41136708
PMCPMC12552660

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.