A Comparative Assessment of Online Machine Translation Software in Translating English Medical Terms into Persian

With the ever-increasing use of online translators, it is more crucial than ever to evaluate their efficiency in various fields, including the medical field, where terminology can pose significant challenges in translation due to the technical and specialized nature of these terms. Regarding the significance of the issue, this study compared the performance of four free online machine translation software (Google Translate, Yandex, Aryanpour, and Faraazin) in translating English medical terms into Persian. The study employed a comparative corpus-based, quantitative approach to evaluate the accuracy, efficiency, and precision of each machine translation tool. Three hundred medical phrases were randomly selected from the Dorland's Medical Dictionary to be machine translated, analyzed, and compared. The Dorland medical dictionary was used as the criterion in this study. The results identified Google Translate as the most effective and precise tool, providing more accurate translations compared to the other translation tools. The study also demonstrated that although the proportions of ‘proper’ translation were similar across all four MT tools, the proportions of ‘improper’ translation and ‘untranslated’ items were significantly different across the tools. Hence, it can be inferred that while MT tools can provide a reliable translation of most medical terms, it is imperative to exercise caution when using these tools for critical and high-stakes medical contexts.

Institutions

Publication Details

Journal
OICC Press Journals
Published
2025-09-01
DOI
https://doi.org/10.57647/jntell.2025.si-06
Primary Topic
Translation Studies and Practices
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Comparative Assessment of Online Machine Translation Software in Translating English Medical Terms into Persian

OICC Press Journals
Translation Studies and Practices
article

A Comparative Assessment of Online Machine Translation Software in Translating English Medical Terms into Persian

article en

Abstract

With the ever-increasing use of online translators, it is more crucial than ever to evaluate their efficiency in various fields, including the medical field, where terminology can pose significant challenges in translation due to the technical and specialized nature of these terms. Regarding the significance of the issue, this study compared the performance of four free online machine translation software (Google Translate, Yandex, Aryanpour, and Faraazin) in translating English medical terms into Persian. The study employed a comparative corpus-based, quantitative approach to evaluate the accuracy, efficiency, and precision of each machine translation tool. Three hundred medical phrases were randomly selected from the Dorland's Medical Dictionary to be machine translated, analyzed, and compared. The Dorland medical dictionary was used as the criterion in this study. The results identified Google Translate as the most effective and precise tool, providing more accurate translations compared to the other translation tools. The study also demonstrated that although the proportions of ‘proper’ translation were similar across all four MT tools, the proportions of ‘improper’ translation and ‘untranslated’ items were significantly different across the tools. Hence, it can be inferred that while MT tools can provide a reliable translation of most medical terms, it is imperative to exercise caution when using these tools for critical and high-stakes medical contexts.

OICC Press Journals
Islamic Azad University, Tehran (IR), Islamic Azad University South Tehran Branch (IR), Islamic Azad University North Tehran Branch (IR)
Quality Education
Openalex Percentile: Top 33%
Translation Studies and Practices
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.