The DOI matches its Crossref record; 19 of the 25 references it cites were found in a registry or an index.
The use of artificial intelligence to improve the scientific writing of non-native english speakers
Open accessAbstract
OBJECTIVE: Scientific writing in English is a daunting task for non-native English speakers. The challenges of writing in a foreign language are evident in the scientific literature where texts by non-native English-speaking scientists tend to be less clear and succinct, contain grammatical errors, and are often rejected by prestigious journals. METHODS: We conducted a non-systematic review of the most recent literature using the terms "Artificial Intelligence," "Scientific Writing," and "Non-English Speaking" to create a narrative review. RESULTS: Artificial intelligence can be a solution to improve scientific writing, especially for non-native English-speaking scientists. Artificial intelligence can assist in the search for pertinent scientific papers, generate summaries, and help with the writing of different sections of the manuscript, including the abstract, introduction, methods, results, and discussion. Artificial intelligence-based programs can correct grammatical errors and improve writing style, both of which are particularly helpful for non-native English speakers. Two artificial intelligence programs that can help with the search for pertinent scientific papers on the internet are Elicit and ResearchRabbit. Scispace Copilot can be used to summarize the retrieved reference. The artificial intelligence software programs such as Grammarly and Paperpal can correct grammatical and spelling errors, while ChatGPT can also restructure sentences and paragraphs, reword text, and suggest appropriate words and phrases. CONCLUSION: Overall, artificial intelligence can be an effective tool to improve the clarity, style, and coherence of scientific writing, helping non-native English-speaking scientists to communicate their research more effectively.
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“Our observations align with those of others who investigated AI platforms for writing and research objectives, as they found that Elicit and SciSpace are far more dedicated to searching for scientific papers and summarizing references [40].”
From Reference Hallucination Score for Medical Artificial Intelligence Chatbots: Development and Usability Study (2024) · cites this paper as [40]
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Cites
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BackgroundArtificial intelligence in scientific writing: a friend or a foe?
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Journals take up arms against AI-written text
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Cited by
Sorted by citations
Using artificial intelligence in academic writing and research: An essential productivity tool
Khalifa, M., & Albadawy, M. (2024) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2024.100145 · Cited by 580
Aljamaan, F., Temsah, M.-H., Altamimi, I., Al-Eyadhy, A., Jamal, A., Alhasan, K., Mesallam, T. A., Farahat, M., & Malki, K. H. (2024) · JMIR Medical Informatics · doi:10.2196/54345 · Cited by 211
“Our observations align with those of others who investigated AI platforms for writing and research objectives, as they found that Elicit and SciSpace are far more dedicated to searching for scientific papers and summarizing references [40].”
ResultLi, B., Tan, Y. L., Wang, C., & Lowell, V. L. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100445 · Cited by 217
Artificial Intelligence in Peer Review: Enhancing Efficiency While Preserving Integrity
Doskaliuk, B., Zimba, O., Yessirkepov, M., Klishch, I., & Yatsyshyn, R. (2025) · Journal of Korean Medical Science · doi:10.3346/jkms.2025.40.e92 · Cited by 88
“18 By correcting language issues, the AI allows reviewers to focus on the more critical scientific content, such as methodology, data interpretation, and overall coherence of the manuscript.”
Potential applications and implications of large language models in primary care
Andrew, A. (2024) · Family Medicine and Community Health · doi:10.1136/fmch-2023-002602 · Cited by 70
“17 Moreover, as LLMs expand in size through self-consistency and reinforcement learning from human feedback, their capabilities advance and allow these models to learn and extract more knowledge from their training data, enabling them to perform tasks that they were not explicitly trained for.”
Background
Metadata
- Authors
- Giglio, A. D., & Costa, M. U. P. D.
- Affiliations
- Fundação do ABC, Brazil
- Biblioteca da Sociedade Brasileira de Oncologia Clínica, Brazil
- Venue
- Revista da Associação Médica Brasileira
- Publisher
- FapUNIFESP (SciELO)
- Published
- 2023
- Volume
- 69
- Issue
- 9
- Pages
- e20230560-e20230560
- ISSN
- 0004-5241 (ISSN-L)
- 1806-9282
- 0104-4230
- PMC
- PMC10508892
- OpenAlex
- W4386831162
- Cited by
- 158
- References
- 25
- Citations by year
- 2026: 53 · 2025: 58 · 2024: 43 · 2023: 3
- FWCI
- 141.57
- Type
- journal article
- Language
- pt
- Open access
- diamond
- doi.org
- Licence
- Locations
- Revista da Associação Médica Brasileira (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Library and Information Sciences › Academic Writing and Publishing
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Education › Artificial Intelligence in Education
- Keywords
- artificial intelligence, scientific writing, non-native English speakers, English as a second language, grammatical error correction, ChatGPT, Grammarly, Elicit, literature searching, text summarization
- MeSH terms
- Artificial Intelligence, Humans, Language, Physicians, Writing, Internet
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