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Interpretation Performance in Chinese and Japanese Medical Consultation Scenarios

A Comparative Study of Interpretation Performance of ChatGPT, Google Translate, and UD Talk in Chinese and Japanese Medical Consultation Scenarios: Contexts of Cardiopulmonary Disease Consultation

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT06934031
Enrollment
42
Registered
2025-04-18
Start date
2025-04-10
Completion date
2025-11-18
Last updated
2026-02-04

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Cardiology, Outpatients, Pulmonology

Keywords

Language barriers, medical communication, medical interpretation, cardiopulmonary disease, translation software

Brief summary

This study aims to compare the translation and interpretation performance of ChatGPT, Google Translate(GT),and UD Talk in cardiopulmonary consultations and analyze their effectiveness in addressing language barriers.The investigators hypothesize that ChatGPT and UD Talk will outperform GT in terms of accuracy and error rates.

Detailed description

Background: Language barriers in healthcare have a significant impact on patient safety, health outcomes, and the quality of healthcare services. With the increase in global migration, more patients are facing language challenges in foreign healthcare systems. Literature shows that providing professional interpretation services significantly improves patient satisfaction and communication quality. However, due to the high usage of ad-hoc interpreters and the shortage of professional interpreters, clinical communication quality has declined. Study Design: This is a one-year, single-center, prospective observational study. Methods: The study will be conducted in the cardiology and pulmonology outpatient clinics at Fu Jen University Hospital. A total of 20 cardiopulmonary disease patients will be enrolled, withtheir consultation sessions recorded and transcribed into Chinese. The study will compare the three tools' performance in terms of semantic accuracy, error types, and severity, and analyze their feasibility in clinical practice. Further analysis will involve satisfaction surveys from both professional interpreters and non-native speakers living in Taiwan. The translation results will be evaluated for accuracy and error rates by 8 professional medical interpreters, with a satisfaction survey completed by a total of 14 non-experts. The evaluation tool will be based on the assessment rubrics from the National Accreditation Authority for Translators and Interpreters. Effect: It is expected that ChatGPT and UD Talk will show better translation accuracy and interpretation quality compared to GT. UD Talk is anticipated to perform better than the other two tools in real-time interpretation. The study results will provide valuable insights for future medical interpretation training and clinical applications.

Interventions

OTHERtranslation software

ChatGPT, Google Translate, and UD talk

Sponsors

Fu Jen Catholic University
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes

Inclusion criteria

First part Inclusion Criteria: * Age over 18 years * Patients visiting the Cardiology or Pulmonology Department at Fu Jen Catholic University Hospital from December 2024 to November 2025

Exclusion criteria

* Patients unable to communicate effectively as assessed by the physician * Refusal to participate in the study Second part Inclusion Criteria: * Age over 18 years * Completed the Japanese-Chinese medical interpretation training course and has at least two years of clinical experience

Design outcomes

Primary

MeasureTime frameDescription
Translation Assessment Scale Score4 months6-point Likert scale, with 5 representing the highest score and 0 representing the lowest.

Countries

Taiwan

Contacts

PRINCIPAL_INVESTIGATORKe-Yun Chao, PhD

Fu Jen Catholic University

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026