10.57647/JNTELL.2025.si-06

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

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  • Masoud Ghavidast1,
  • Mahnaz Azad*2,
  1. Department of Translation Studies, NT.C., Islamic Azad University, Tehran, Iran
  2. Department of English Language Teaching, ET.C., Islamic Azad University, Tehran, Iran

Received: 2025-09-01

Revised: 2025-09-27

Accepted: 2025-09-30

Published in Issue 2025-10-19

Copyright (c) 2025 Masoud Ghavidast, Mahnaz Azad (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Ghavidast, M., & Azad, M. (2025). A Comparative Assessment of Online Machine Translation Software in Translating English Medical Terms into Persian. Journal of New Trends in English Language Learning (JNTELL), 4. https://doi.org/10.57647/JNTELL.2025.si-06
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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.

Keywords

  • Online machine translation,
  • Google Translate,
  • Medical terminology,
  • Yandex Translate
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References

  1. Aiken, M., & Balan, S. (2011). An analysis of Google Translate accuracy. Translation J., 16(2). Available from: www.bokorlang.com/journal/56google.htm.
  2. Archer, J. (2002). Internationalisation, technology and translation. Perspectives: Studies in Translatology, 10(2), 87–117. https://doi.org/10.1080/0907676X.2002.9961437.
  3. Arnold, D. L., Balkan, S., Meijer, R.L., Humphreys, L., & Sadler, L. (1994). Machine translation: An introductory guide. NCC Blackwell.
  4. Aryanpour (2023). English to Persian Dictionary: Aryanpour English to Farsi Translator. Retrieved February 20, 2023, from https://aryanpour.com
  5. Bahdanau, D., Cho, K., & Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate. In Proceedings of the 3rd International Conference on Learning Representations (May 7–9), San Diego, USA.
  6. Betancourt, J. R., & Jacobs, E. A. (2000). Language barriers to informed consent and confidentiality: the impact on women's health. Journal of American Medical Women’s Association, 55(5), 294-295. https://www.researchgate.net/publication/12256281
  7. Birkenbeuel, J., Joyce, H. J., Sahyouni, R., Cheung, D., Maducdoc, M., Mostaghni, N., … Lin, H. W. (2021). Google translate in healthcare: preliminary evaluation of transcription, translation, and speech synthesis accuracy. BMJ Innovations, 7(2), 422–429. https://doi.org/10.1136/bmjinnov-2019-000347.
  8. Brown, P., Cocke, J., Della Pietra, S. A., Della Pietra, V. J., Jelinek, F., Mercer, R., & Roossin, P. (1990). A statistical approach to language translation. Computational Linguistics, 16(2), 71-76. http://dx.doi.org/10.3115/991635.991651.
  9. Buzarna-Tihenea, G.A. (2015a). Several aspects regarding the specialized translation of medical texts. From theory to practice. In Ovidius University Annals, Economic Sciences Series (pp. 17-21). Ovidius University Press.
  10. Buzarna-Tihenea, G. A. (2015b). Procedures and difficulties in the translation of medical texts. An English-Romanian case study. In Discourse as a Form of Multiculturalism in Literature and Communication, Section: Language and Discourse (pp. 164-175). Arhipelag XXI Press.
  11. Chen, X., Acosta, S., & Barry, A. E. (2016). Evaluating the accuracy of Google Translate for diabetes education material. JMIR Diabetes, 1(1), e3. https://doi.org/10.2196/diabetes.5848.
  12. Chiang, D. (2007). Hierarchical phrase-based translation. Computational Linguistics, 33(2), 201-228. https://doi.org/10.1162/coli.2007.33.2.201.
  13. Church, K.W., & Mercer, R.L. (1993). Introduction to the special issue on computational linguistics using large corpora. Computational Linguistics, 19(1), 1-24. MIT Press. https://aclanthology.org/J93-1001/
  14. Merschel, L. M., Reisinger, D., & Clifford, J. (2013). Meeting the challenges of machine translation. The Language Educator, 8, 44–47. https://www.researchgate.net/publication/362177070
  15. Costa-jussà, M.R., Farrús, M., & Serrano Pons, J. (2012). Machine translation in medicine: A quality analysis of statistical machine translation in the medical domain. Advanced Research in Scientific Areas, 3(7), 1995-1998. https://www.upf.edu/documents/2981382/2985826/
  16. Daniele, F. (2019). Performance of an automatic translator in translating medical abstracts. Heliyon, 5(10). https://doi.org/10.1016/j.heliyon.2019.e02687
  17. Deilen, S., Lapshinova-Koltunski, E., Hernández Garrido, S., Maaß, Ch., Hörner, J., Theel, V., & Ziemer, S. (2024). Towards AI-supported health communication in plain language: Evaluating intralingual machine translation of medical texts. In Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024 (pages 44–53), Torino, Italia. ELRA and ICCL.
  18. Dew, K. N., Turner, A. M., Choi, Y. K., Bosold, A., & Kirchhoff, K. (2018). Development of machine translation technology for assisting health communication: A systematic review. Journal of Biomedical Informatics, 85, 56-67. https://doi.org/10.1016/j.jbi.2018.07.018.
  19. Dong, D., Wu, H., He, W., Yu, D., & Wang, H. (2015). Multi-task learning for multiple language translation. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Jul 26–31), Beijing, China.
  20. Faraazin. (2023). Online translation from English to Persian and vice versa. Retrieved February 23, 2023, from https://www.faraazin.ir/
  21. Gehring, J., Auli, M., Grangier, D., Yarats, D., & Dauphin, Y.N. (2017). Convolutional sequence to sequence learning. In Proceedings of the 34th International Conference on Machine Learning; 2017 Aug 6–11; Sydney, NSW, Australia. https://doi.org/10.48550/arXiv.1705.03122
  22. Gharsa, M. A. (2015). The problem of translating medical terms from English into Arabic. Unpublished doctoral dissertation, Durham University. Durham, United Kingdom. Retrieved May 2024, from https://etheses.dur.ac.uk/11166.html.
  23. Hellmich, E. A. (2019). Language teacher beliefs about technology: expanding the ecology. International Journal of Computer-Assisted Language Learning and Teaching, 9, 1–17. https://doi.org/10.4018/IJCALLT.2019100101
  24. Honari, M. (2018). The problems of machine translation in English-Persian and Persian-English translation of technical and scientific texts based on Waddington's Method A. Unpublished master's thesis, Islamic Azad University of Bandar Abbas, Iran.
  25. Hutchins, W.J. (2000). The IAMT certification initiative and defining translation system categories. In Proceedings of 5th EAMT Workshop: Harvesting Existing Resources, Ljubljana, Slovenia. European Association for Machine Translation. https://aclanthology.org/2000.eamt-1.8/
  26. Kaliyadan, F., & Pillai, S. G. (2010). The use of Google language tools as an interpretation aid in cross-cultural doctor-patient interaction: A pilot study. Informatics in Primary Care, 18(2), 141-143. http://dx.doi.org/10.14236/jhi.v18i2.764.
  27. Karwacka, W. (2015). Medical translation. In Ł. Bogucki, S. Goźdź-Roszkowski, P. Stalmaszczyk (Eds.), Ways to translation (pp 271-298). Wydawnictwo Uniwersytetu Łódzkiego.
  28. Khadem Olhosseiny, Z. S., & Askari, M.I. (2024). CAT tools in translation proficiency development: EFL students' perceptions in focus. JNTELL, 3(2), 35-68. DOI: 10.71528/2024.24011091
  29. Khanna, R. R., Karliner, L. S., Eck, M., Vittinghoff, E., Koenig, C. J., & Fang, M. C. (2011). Performance of an online translation tool when applied to patient educational material. Journal of Hospital Medicine, 6(9), 519-25. https://doi.org/10.1002/jhm.898.
  30. Khoong, E. C., Steinbrook, E., Brown, C., & Fernandez, A. (2019). Assessing the use of Google Translate for Spanish and Chinese translations of emergency department discharge instructions. JAMA Internal Medicine, 179(4), 580–582. https://doi.org/10.1001/jamainternmed.2018.7653.
  31. Koehn, P., & Knowles, R. (2017). Six challenges for neural machine translation. In Proceedings of the First Workshop on Neural Machine Translation, 2017 Aug 4, Vancouver, BC, Canada.
  32. Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., … Herbst, E. (2007). Moses: open-source toolkit for statistical machine translation. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (Jun 25–27), Prague, Czech Republic.
  33. Kroulek, A. (2016). 11 Google Translate facts you should know. Retrieved April 8, 2023, from https://www.k-international.com/blog/google-translate-facts/.
  34. McGuire, N. (2018). How accurate is Google Translate in 2018? Argo Translation. Retrieved April 8, 2024, from https://www.argotrans.com/blog/accurate-google-translate-2018/.
  35. Mehandru, N., Robertson, S., & Salehi, N. (2022). Reliable and safe use of machine translation in medical settings. ACM Conference on Fairness, Accountability, and Transparency, 2022. Retrieved from https://doi.org/10.1145/3531146.3533244.
  36. Moberly, T. (2018). Doctors are cautioned against using Google Translate in consultations. British Medical Journal, 363. https://doi.org/10.1136/bmj.k4546.
  37. MOGI’s Translation Team. (2011). Technical translation. Retrieved April 2023, from https://www.moi.translations.com/technical-documentation-Translation.html.
  38. Montero‐Martínez, S., & de Quesada, M. G. (2003). Terminological analysis for translation. Perspectives, 11(4), 293–314. https://doi.org/10.1080/0907676X.2003.9961482
  39. Munassar Awadh, A.N. (2024). Challenges and strategies of translating scientific texts: A comparative study of human translation and artificial intelligence. Educational Administration: Theory and Practice, 30(4), 9898–9909. https://doi.org/10.53555/kuey.v30i5.4147
  40. Nădrag, L. (2016). Issues raised by the English-Romanian translation of cardiovascular terminology. In I. Boldea & D.M. Buda (Eds.), Convergent discourses : Exploring the contexts of communication (pp. 272-278). Arhipelag XXI Press.
  41. Nagao, M. (1984). A framework for a mechanical translation between Japanese and English by analogy principle. In A. Elithorn & R. Banerji (eds.), Artificial and human intelligence (pp.173-180). Elsevier North-Holland, Inc.
  42. Newman Dorland, W. (1890). Dorland’s Medical Dictionary. [Translated by H. Namavar & M. Zarabi, 2020, 32nd edition]. http://elsevierhealth.com/product.jsp?isbn=9781455709854&dmnum=12449
  43. National Research Council. (1966). Language and machines: Computers in translation and linguistics. The National Academies Press. https://doi.org/10.17226/9547.
  44. Nord, C. (1997). Text analysis in translation: Theory, methodology, and didactic application of a model for translation-oriented text analysis. Rodopi.
  45. Och, F. J., & Ney H. (2003). A systematic comparison of various statistical alignment models. Computational Linguistics, 29(1), 19-51. http://dx.doi.org/10.1162/089120103321337421.
  46. Pekcoşkun Güner, S. (2020). Translation of medical texts: challenges and solutions. The Journal of Social Sciences, 340-351. http://dx.doi.org/10.29228/SOBIDER.43857
  47. Patil, S., & Davies, P. (2014). Use of Google Translate in medical communication: evaluation of accuracy. British Medical Journal, 349. DOI: https://doi.org/10.1136/bmj.g7392
  48. Rahmanian, A. (2018). The problems with medical translation terminology based on Reiss’s Model. Unpublished master's thesis, Islamic Azad University of Bandar Abbas, Iran.
  49. Randhawa, G., Ferreyra, M., Ahmed, R., Ezzat, O., & Pottie, K. (2013). Using machine translation in clinical practice. Canadian Family Physician, 59(4), 382-383. PMID: 23585608; PMCID: PMC3625087.
  50. Sager, J.C. (1994). Language engineering and translation: Consequences of automation. John Benjamins Publishing Company. https://doi.org/10.1075/target.8.1.17sch
  51. Sedaghat Rostami, M., & Bigdeli, A. (2016). Translation of social sciences texts: problems and solutions. Journal of Iranian Social Studies, 10(2.3), 57-76. http://www.jss-isa.ir/article_31590_en.html
  52. Somers, H.L. (2003). Introduction. In H.L. Somers (Eds.), Computers and translation: A translator’s guide (pp. 1-11). John Benjamins.
  53. Spechbach, H., Mallem, I. S. H., Gerlach, J., Tsourakis, N., & Bouillon, P. (2017). Comparison of the quality of two speech translators in emergency settings: A case study with standardized Arabic-speaking patients with abdominal pain. In Proceedings of European Congress of Emergency Medicine (EUSEM 2017). Athens, Greece.
  54. Sutskever, I., Vinyals, O., & Le, Q.V. (2014). Sequence-to-sequence learning with neural networks. In Proceedings of the 27th International Conference on Neural Information Processing Systems (8–13), Montreal, QC, Canada.
  55. Taleghani, M., & Pazouki, E. (2018). Free online translators: A comparative assessment in terms of idioms and phrasal verbs. International Journal of English Language & Translation Studies, 6(1), 15-19. http://www.eltsjournal.org
  56. Turner, A. M., Mandel, H., & Capurro, D. (2013). Local health department translation processes: potential of machine translation technologies to help meet needs. In AMIA annual symposium proceedings, 2013, American Medical Informatics Association.
  57. Turner, K. (2016). Google Translate is getting really, really accurate. Washington Post, October 3. Retrieved May 8, 2024, from https://www.washingtonpost.com/news/innovations/wp/2016/10/03/google-translate-is-getting-really-really-accurate.
  58. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N. … Polosukhinet, I. (2017). Attention is all you need. In Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017); 2017 Dec 4–9, Long Beach, CA, USA.
  59. Vieira, L. N., O'Hagan, M., & O'Sullivan, C. (2020). Understanding the societal impacts of machine translation: a critical review of the literature on medical and legal use cases. Information, Communication & Society, 11, 1515-1532. https://doi.org/10.1080/1369118X.2020.1776370.
  60. Wang, H. (2006). Multi-strategy machine translation. In Y.Q. Cao & M.S. Sun (Eds.), Frontiers of Chinese information processing (pp. 45-52). Tsinghua University Press.
  61. Wang, H., Wu, H., He, Z., Huang, L., & Church, K. W. (2022). Progress in machine translation. Engineering, 18, 143-153. https://doi.org/10.1016/j.eng.2021.03.023.
  62. Weng, W.H., Chung, Y. A., & Szolovits, P. (2019). Unsupervised clinical language translation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’19, Association for Computing Machinery (pp. 3121-3131). New York, NY, USA. 10.1145/3292500.3330710
  63. Williams, J., & Chesterman, A. (2002). The map: A beginner’s guide to doing research in translation studies. Saint Jerome Publication.
  64. Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., … Dean, J. (2016). Google’s neural machine translation system: bridging the gap between human and machine translation. https://doi.org/10.48550/arXiv.1810.08398.
  65. Xie, W., Ji, M., Zhao, M., Zhou, T., Yang, F., Qian, X., Chow, C.-Y., Lam, K.-Y., & Hao, T. (2021). Detecting symptom errors in neural machine translation of patient health information on depressive disorders: Developing interpretable Bayesian machine learning classifiers. Front. Psychiatry, 12 (2021). https://doi.org/10.3389/fpsyt.2021.771562
  66. Yandex. (2017, September 14). One model is better than two. Yandex Translate launches a hybrid machine translation system. Retrieved February 23, 2024, from https://yandex.com/company/blog/one-model-is-better-than-two-yu-yandex-translate-launches-a-hybrid-machine-translation-system/
  67. Yandex. (2023). About Yandex Translate. Retrieved February 20, 2024, from https://yandex.com/support/translate/
  68. Yassi, M. (2018). A study of equivalence in English-Persian translation of surgical medical terms based on Newmark’s Model. Unpublished master's thesis, Islamic Azad University of Marvdasht Branch, Iran.
  69. Zappatore, M., & Ruggieri, G. (2024). Adopting machine translation in the healthcare sector: A methodological multi-criteria review. Computer Speech and Language, 84, 101582. https://doi.org/10.1016/j.csl.2023.101582
  70. Zhang, J.J., & Zong, C.Q. (2020). Neural machine translation: challenges, progress, and future. Science China Technological Sciences, 63(10), 2028-2050. https://doi.org/10.48550/arXiv.2004.05809
  71. Zhao, L., Gao, W., & Fang, J. (2021). High-performance English–Chinese machine translation based on GPU-enabled deep neural networks with domain corpus. Appl. Sci., 11 (22), 10915. https://doi.org/10.3390/app112210915
  72. Zhu, Y., Sha, Y., Wu, H., Li, M., Hoffman, R.A., & Wang, M.D. (2022). Proposing causal sequence of death by neural machine translation in public health informatics. IEEE J. Biomed. Health Inf., 26 (2022), 1422-1431. DOI: 10.1109/JBHI.2022.3163013