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Arab World English Journal (AWEJ) Special Issue on CALL Vol. 17, No.12. July 2026 Pp.418-434
Centre for Research in Language and Linguistics, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Selangor, Malaysia
Centre for Research in Language and Linguistics, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Selangor, Malaysia
Centre for Research in Language and Linguistics, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Selangor, Malaysia
This review focuses on how artificial intelligence has been used to support English medical writing among non-native English speakers. The purpose was not to list every available tool, but to identify what the existing literature shows about their uses, benefits, limitations, and risks. Searches were conducted in Google Scholar, Scopus, and PubMed with terms related to artificial intelligence, medical or healthcare writing, and non-native English users. The search first produced 978 records. After duplicate removal, title and abstract screening, and full-text assessment, 24 sources were included in the review. These sources mainly discussed four forms of AI support: grammar and style checking, terminology and lexical assistance, summarisation or simplification, and large language model-based drafting or revision. Across the reviewed literature, AI tools were most often linked to grammar correction, vocabulary choice, readability, writer confidence, and the simplification of medical information for non-specialist readers. These benefits were useful but mostly short-term. Many studies used small samples, brief periods of tool use, or outcome measures that could not show whether learners became better independent writers. It is safer to view AI as a writing support tool rather than a replacement for teachers, supervisors, or writing instruction. Its use in English medical writing education should be guided by clear rules, disclosure of AI assistance, and careful checking factual accuracy in medical text.
Shen, W., Hamat, A., Che Abdul Rahman, A. N., & Wang, J. (2026). Artificial Intelligence in Medical Writing Education for Non-Native English Speakers: A Systematic Review of Applications, Benefits, and Risks. Arab World English Journal (AWEJ) Special Issue on CALLVol.17. (12)418-434. https://dx.doi.org/10.24093/awej/call12.24
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Wei Shen is a PhD candidate in the English Language Studies Programme at the Centre for Research in Language and Linguistics, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Malaysia. Her research areas are corpus linguistics, second language acquisition, and translation. Email: P120860@siswa.ukm.edu.my ORCID: https://orcid.org/0009-0005-3791-5719
Dr. Afendi Hamat is an Associate Professor at the Centre for Research in Language and Linguistics, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Malaysia. His research areas are e-learning and web-based CALL. He has developed various e-learning platforms such as the Second Language Instruction and Management System (SLIM) and the intelligent English Language Literacy System (iELLS). Email: fendi@ukm.edu.my ORCID: https://orcid.org/0000-0003-2060-9203
Dr. Anis Nadiah Che Abdul Rahman is a senior lecturer at the Centre for Research in Language and Linguistics, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Malaysia. Her area of expertise is in corpus linguistics and semantics. Her research interests are in political and social media discourse. Email: aniscar@ukm.edu.my
ORCID: https://orcid.org/0000-0001-7530-1113
Jun Wang is an Associate Professor in the School of Foreign Studies at Yanshan University, China. Her research area is TEFL (Teaching English as a Foreign Language). Email: wywy@ysu.edu.cn ORCID: https://orcid.org/0009-0005-6246-7030