OICC Press Journal of New Trends in English Language Learning (JNTELL) 2821-2290 4 2025 10 19 A Comparative Assessment of Online Machine Translation Software in Translating English Medical Terms into Persian 10.57647/JNTELL.2025.si-06 EN Masoud Ghavidast Department of Translation Studies, NT.C., Islamic Azad University, Tehran, Iran Mahnaz Azad Department of English Language Teaching, ET.C., Islamic Azad University, Tehran, Iran https://orcid.org/0000-0001-5782-5108 Journal Article 2025 10 19 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.