ArticleJMIR diabetes2017
Machine or Human? Evaluating the Quality of a Language Translation Mobile App for Diabetes Education Material.
Article in JMIR diabetes, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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.
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.
Who cites it
20 citing papers in PubMed, 38 citations in OpenAlex.
- Large language models' performance in answering common patient questions about colonoscopy: an expert-based evaluation.BMC gastroenterology · 2026Observational
- Wearable Real-Time Translation in Rhinology: A Comparative Study of Accuracy and Efficiency.International forum of allergy & rhinology · 2026Article
- Can ChatGPT provide reliable guidance to families of children with Duchenne Muscular Dystrophy? A dual perspective of parents and experts.BMC pediatrics · 2026Article
- Evaluating the performance of ChatGPT in responding to myotonic dystrophy type 1 patient inquiries: a specialist physician-based study.BMC neurology · 2026Article
- [Diabetes and migration - Recommendations for the practice (Update 2026)].Wiener klinische Wochenschrift · 2026Article
- MedCOD: Enhancing English-to-Spanish Medical Translation of Large Language Models Using Enriched Chain-of-Dictionary Framework.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2025Article
- Accuracy of Artificial Intelligence vs Professionally Translated Discharge Instructions.JAMA network open · 2025Article
- Assessing the role of large language models in adolescent idiopathic scoliosis care: a comparison between ChatGPT and Google Gemini.Acta orthopaedica et traumatologica turcica · 2025Article
- Artificial intelligence in clinical settings: a systematic review of its role in language translation and interpretation.Annals of translational medicine · 2024Review
- Using Voice-to-Voice Machine Translation to Overcome Language Barriers in Clinical Communication: An Exploratory Study.Journal of general internal medicine · 2024Article
- Customization options in consumer health information materials on type-2 diabetes mellitus-an analysis of modifiable features in different types of media.Frontiers in public health · 2024Review
- ChatGPT's potential to support home care for patients in the early period after orthopedic interventions and enhance public health.Joint diseases and related surgery · 2024Article
- Impacts of language barriers on healthcare access and quality among Afaan Oromoo-speaking patients in Addis Ababa, Ethiopia.BMC health services research · 2023Article
- Article
- Evaluation of a Language Translation App in an Undergraduate Medical Communication Course: Proof-of-Concept and Usability Study.JMIR mHealth and uHealth · 2021Article
- Promoting HIV, Hepatitis B Virus, and Hepatitis C Virus Screening Among Migrants With a Language Barrier: Protocol for the Development and Evaluation of an Electronic App (Apidé).JMIR research protocols · 2021Article
- Associations Between Immigration-Related User Factors and eHealth Activities for Self-Care: Case of First-Generation Immigrants From Pakistan in the Oslo Area, Norway.JMIR public health and surveillance · 2019Article
- Evaluating the Usefulness of Translation Technologies for Emergency Response Communication: A Scenario-Based Study.JMIR public health and surveillance · 2019Article
- Assessing Health Literacy Among Chinese Speakers in the U.S. with Limited English Proficiency.Health literacy research and practice · 2018Article
- Health Literacy and Use and Trust in Health Information.Journal of health communication · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiabetes is a major health crisis for Hispanics and Asian Americans. Moreover, Spanish and Chinese speakers are more likely to have limited English proficiency in the United States. One potential tool for facilitating language communication between diabetes patients and health care providers is technology, specifically mobile phones.
objectivePrevious studies have assessed machine translation quality using only writing inputs. To bridge such a research gap, we conducted a pilot study to evaluate the quality of a mobile language translation app (iTranslate) with a voice recognition feature for translating diabetes patient education material.
methodsThe pamphlet, "You are the heart of your family…take care of it," is a health education sheet for diabetes patients that outlines three recommended questions for patients to ask their clinicians. Two professional translators translated the original English sentences into Spanish and Chinese. We recruited six certified medical translators (three Spanish and three Chinese) to conduct blinded evaluations of the following versions: (1) sentences interpreted by iTranslate, and (2) sentences interpreted by the professional human translators. Evaluators rated the sentences (ranging from 1-5) on four scales: Fluency, Adequacy, Meaning, and Severity. We performed descriptive analyses to examine the differences between these two versions.
resultsCronbach alpha values exhibited high degrees of agreement on the rating outcomes of both evaluator groups: .920 for the Spanish raters and .971 for the Chinese raters. The readability scores generated using MS Word's Flesch-Kincaid Grade Level for these sentences were 0.0, 1.0, and 7.1. We found iTranslate generally provided translation accuracy comparable to human translators on simple sentences. However, iTranslate made more errors when translating difficult sentences.
conclusionsAlthough the evidence from our study supports iTranslate's potential for supplementing professional human translators, further evidence is needed. For this reason, mobile language translation apps should be used with caution.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.