Limitations and mitigation strategies for using generative artificial intelligence in medical writing: a narrative review.
Purpose: Large language models (LLMs) improve medical writing efficiency but introduce methodological, ethical, and legal risks. This review examines current evidence on the limitations of LLM-assisted medical writing and proposes principles for its responsible integration into biomedical research....
| Publicado en: | Journal of the Korean Medical Association / Taehan Uisa Hyophoe Chi Vol. 69; no. 2; pp. 118 - 126 |
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| Formato: | review Journal Article |
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Korean Medical Association
Feb2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=192349755&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 192349755 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19758456 B9M7 jtl: Journal of the Korean Medical Association / Taehan Uisa Hyophoe Chi issn: 19758456 maglogo: N pubinfo: dt: Feb2026 vid: 69 iid: 2 pid: 64891 pub: Korean Medical Association place: , <Blank> artinfo: ui: 192349755 192349755 192349755 10.5124/jkma.25.0163 192349755 ppf: 118 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Limitations and mitigation strategies for using generative artificial intelligence in medical writing: a narrative review. aug: au: Jeon, Ki-Hyun affil: Division of Cardiology, Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea sug: subj: Natural Language Processing Utilization Medical Writing Research, Medical Edit and Review Manuscripts Readability Information Retrieval Authorship Accountability Plagiarism Data Security Natural Language Processing Ethical Issues Citation Analysis Bibliography and References Workflow Computerized Literature Searching ab: Purpose: Large language models (LLMs) improve medical writing efficiency but introduce methodological, ethical, and legal risks. This review examines current evidence on the limitations of LLM-assisted medical writing and proposes principles for its responsible integration into biomedical research. Current concepts: LLMs are commonly used for draft generation, language editing, literature summarization, reference handling, statistical code generation, and manuscript structuring. Studies consistently report improved readability and reduced writing time, particularly among non-native English-speaking authors. However, recurrent challenges include factual hallucinations; fabricated or inaccurate citations; incomplete retrieval of recent literature due to training cutoffs; prompt-sensitive statistical errors; ambiguity regarding authorship and accountability; risks of unintended plagiarism; and concerns related to patient data privacy. These limitations arise from the probabilistic nature of LLMs and their lack of intrinsic fact-verification mechanisms or ethical reasoning. Discussion and conclusion: Risks associated with LLM use vary by manuscript stage and therefore require differentiated oversight. LLMs should be confined primarily to language refinement rather than fact generation, and literature-related outputs must be verified against primary sources, preferably using retrieval-augmented tools. Statistical analyses should remain under human control, with independent validation of all outputs. Ethical governance requires transparent disclosure of LLM use, clear assignment of human responsibility, and strict safeguards for sensitive data. A dual framework combining human-in-the-loop and human-on-the-loop oversight offers a pragmatic model for balancing efficiency with scientific rigor. When positioned as augmentative tools rather than autonomous agents, LLMs can be responsibly integrated into medical research without compromising integrity or reproducibility. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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