Cited by 46Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIAcademic integrity and plagiarism

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Artificial intelligence in scientific medical writing: Legitimate and deceptive uses and ethical concerns

Open access

Abstract

The debate surrounding the integration of artificial intelligence (AI) into scientific writing has already attracted significant interest in medical and life sciences. While AI can undoubtedly expedite the process of manuscript creation and correction, it raises several criticisms. The crossover between AI and health sciences is relatively recent, but the use of AI tools among physicians and other scientists who work in the life sciences is growing very fast. Within this whirlwind, it is becoming essential to realize where we are heading and what the limits are, including an ethical perspective. Modern conversational AIs exhibit a context awareness that enables them to understand and remember any conversation beyond any predefined script. Even more impressively, they can learn and adapt as they engage with a growing volume of human language input. They all share neural networks as background mathematical models and differ from old chatbots for their use of a specific network architecture called transformer model [1]. Some of them exceed 100 terabytes (TB) (e.g., Bloom, LaMDA) or even 500 TB (e.g., Megatron-Turing NLG) of text data, the 4.0 version of ChatGPT (GPT-4) was trained with nearly 45 TB, but stays updated by the internet connection and may integrate with different plugins that enhance its functionality, making it multimodal.

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Crossref · 10.1016/j.ejim.2024.07.012

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  • Prior studies have mostly discussed potential applications and ethical implications of LLMs for scientific writing in medicine overall and, when performance was measured, analyzed often only one (typically proprietary) LLM.

    “Prior studies have mostly discussed potential applications and ethical implications of LLMs for scientific writing in medicine overall and – when performance was measured – analyzed often only one (typically proprietary) LLM.12–17”

    From Open-source versus proprietary large language models for academic writing in medicine: A controlled evaluation of DeepSeek-R1 and ChatGPT-o1 in neuroradiology (2026) · cites this paper as [15]

    Sections 2.2–2.3 and 3 discuss applications and ethical concerns of LLMs in scientific medical writing, supporting that part of the citation. The paper does not establish that prior writing-performance studies often evaluated only one, typically proprietary, LLM; its multi-model performance table concerns medical question answering instead. Page numbers are unavailable in our copy. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 11

  1. Briganti G. How ChatGPT works: a mini review. European archives of oto-rhino-laryngology: official journal of the European Federation of Oto-Rhino-Laryngological Societies. 2023. · DOI
  2. Sanderson, 2024, Science's fake-paper problem: high-profile effort will tackle paper mills, Nature, 626, 17, 10.1038/d41586-024-00159-9 · DOI
  3. Kiester, 2022, Artificial intelligence behind the scenes: PubMed's Best Match algorithm, Journal of the Medical Library Association: JMLA, 110, 15, 10.5195/jmla.2022.1236 · DOI
  4. Ingley, 2023, Leveraging AI tools to develop the writer rather than the writing, Trends in ecology & evolution, 38, 785, 10.1016/j.tree.2023.05.007 · DOI
  5. Brainard, 2023, New tools show promise for tackling paper mills, Science, 380, 568, 10.1126/science.adi6513 · DOI
  6. Gao, 2023, Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers, NPJ digital medicine, 6, 75, 10.1038/s41746-023-00819-6 · DOI · Paper page
  7. Ioannidis, 2005, Why most published research findings are false, PLoS Med, 2, e124, 10.1371/journal.pmed.0020124 · DOI · Paper page
  8. Zhang, 2023, A Critical Review of Inductive Logic Programming Techniques for Explainable AI, IEEE transactions on neural networks and learning systems
  9. O'Connor, 2023, Corrigendum to “Open artificial intelligence platforms in nursing education: Tools for academic progress or abuse?”, Nurse Educ. Pract., 66
  10. 2023, Tools such as ChatGPT threaten transparent science; here are our ground rules for their use, Nature, 613, 612, 10.1038/d41586-023-00191-1 · DOI · Paper page
  11. Telenti, 2024, Large language models for science and medicine, European journal of clinical investigation, e14183, 10.1111/eci.14183 · DOI

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Metadata

Authors
Ramoni, D., Sgura, C., Liberale, L., Montecucco, F., Ioannidis, J. P. A., & Carbone, F.
Affiliations
  • University of Genoa (IT)
  • Ospedale Policlinico San Martino (IT)
  • Stanford University (US)
Corresponding author
Carbone, Federico
Venue
European Journal of Internal Medicine
Publisher
Elsevier BV
Published
2024-09
Volume
127
Pages
31-35
ISSN
0953-6205 (ISSN-L)
OpenAlex
W4400952216
Cited by
46
References
4
Citations by year
2026: 25 · 2025: 20 · 2024: 1
FWCI
3.71
Type
journal article
Language
en
Open access
Locations
  • European Journal of Internal Medicine (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion, other-oa)
Funders
  • Ministero della Salute
  • Ministero dell'Istruzione dell'Universita e della Ricerca
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Social Sciences › Education › Academic integrity and plagiarism
Keywords
artificial intelligence, scientific writing, medical writing, AI ethics, ChatGPT, large language models, transformer models, AI-generated text, research integrity
MeSH terms
Artificial Intelligence, Humans, Medical Writing

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