The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index; none of the 2 sources its claims cite is checked for support yet.
Responsible Use of Generative Artificial Intelligence for Research and Writing: Summarizing ICMJE Guideline
Open accessOverview
CoChat Summary
TL;DR
This letter summarizes ICMJE guidance on responsible use of generative AI in research and writing, arguing that AI writing assistants are ethical when used transparently and with human oversight. It presents author responsibilities in Table 1 and refers readers to Fig. 1 for common author queries and ICMJE-based replies.
Key findings
- The authors state that using AI as a writing assistant is “not unethical when employed responsibly and transparently,” and that it can enhance efficiency and manuscript quality without compromising research integrity.
- Generative AI can help with grammar and style corrections, language improvements, and generating initial drafts based on provided data; these functions are compared to traditional editorial assistance.
- Authors play the central role in responsible AI use. Table 1 lists eight responsibilities: selection, transparency, accuracy, plagiarism, bias, compliance, update, and oversight.
- The authors echo MDPI’s statement that “AI will become the norm, like how the Internet or Google are now.”
Methods
- This is a letter/commentary, not an empirical study; it summarizes the ICMJE guideline on AI use in research and writing.
- The paper presents a table of authors’ responsibilities and a figure on common author queries and replies according to ICMJE guidelines.
- No data collection, sample, or statistical analysis is reported.
Limitations
- The paper states no limitations.
- Evident from the text: it is a brief summary rather than a systematic review or original analysis, and the content of Fig. 1 is not reproduced in the provided text.
- Evident from the text: the recommendations rely on ICMJE and selected external sources, including MDPI, without empirical validation.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
0 Valid · 0 Not valid · 2 Not checked · 4 Own results and interpretations
In the paper
“When AI contributions are properly acknowledged and human oversight is maintained, the use of such technology aligns with ethical research practices [3].”
What the sources say
- Not checked yetcites [3] Mondal, H., & Mondal, S. (2023). Can researchers write their articles by artificial intelligence? Journal of Applied Science and Clinical Practice, 4, 165–167. source
plainstates a possible cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“International Committee of Medical Journal Editors (ICMJE) has published their guidelines for the use of AI in research and writing.”
What the sources say
- Not matchedcites [2] Defining the Role of Authors and Contributors. ICMJE. Available from: https://www.icmje.org/recommendations/browse/roles-and-responsibilities/defining-the-role-of-authors-and-contributors.html. Accessed on 26 June 2024
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
1 reference still to check
A free account verifies the references that are still open.
- Probably not a substantial claim. 4
- It may state two separate claims as one. 1
Citations
Cites
Ganjavi, C., Eppler, M. B., Pekcan, A., Biedermann, B., Abreu, A., Collins, G. S., Gill, I. S., & Cacciamani, G. E. (2024) · BMJ · doi:10.1136/bmj-2023-077192 · Cited by 266
Lower, K., Seth, I., Lim, B., & Seth, N. (2023) · Indian Journal of Orthopaedics · doi:10.1007/s43465-023-00967-7 · Cited by 50
Can Researchers Write their Articles by Artificial Intelligence?
Mondal, H., & Mondal, S. (2023) · Journal of Applied Sciences and Clinical Practice · doi:10.4103/jascp.jascp_44_23 · Cited by 2
Self-citation
Cited by
Assessing the Capability of Large Language Model Chatbots in Generating Plain Language Summaries
Mondal, H., Gupta, G., Sarangi, P. K., Sharma, S., Choudhary, P. K., Juhi, A., Kumari, A., & Mondal, S. (2025) · Cureus · doi:10.7759/cureus.80976 · Cited by 14
Self-citationThe Artificial Intelligence Dilemma in Academic Writing: Balancing Efficiency and Integrity
Mondal, H., Mondal, S., & Jana, S. (2025) · Indian Journal of Cardiovascular Disease in Women · doi:10.25259/ijcdw_86_2024 · Cited by 4
Self-citationArtificial Intelligence in Manuscript Preparation: Are We Becoming Dependent on Machines?
Mondal, H. (2024) · Indian Journal of Nuclear Medicine · doi:10.4103/ijnm.ijnm_130_24 · Cited by 2
Self-citationCorradin, A., Ciccarese, F., Raimondi, V., Urso, L., & Silic‐Benussi, M. (2025) · Proceedings of the World Congress on New Technologies · doi:10.11159/icbb25.161 · Cited by 1
AI in research and publication: prioritizing content over language
Mondal, H. (2024) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-024-09028-7 · Cited by 1
Self-citationGenerative artificial intelligence and editorial ethics: a roadmap for Health & New Media Research
Noh, G. Y. (2025) · Health & New Media Research · doi:10.22720/hnmr.2025.00304
Navigating the crossroads: Embracing and regulating large language model in academic writing
Mondal, H. (2026) · Taiwanese Journal of Obstetrics and Gynecology · doi:10.1016/j.tjog.2025.07.030
Self-citationJain, A., & Gupta, V. (2026) · Current Medicine Research and Practice · doi:10.4103/cmrp.cmrp_163_25
AGUITE, A., Jr. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7019199
Gehling, G. M., & Heinsberg, L. W. (2026) · Biological Research For Nursing · doi:10.1177/10998004261489817
Metadata
- Authors
- Mondal, H., Mondal, S., & Juhi, A.
- Affiliations
- All India Institute of Medical Sciences, Deoghar (IN)
- Government of West Bengal (IN)
- Corresponding author
- Mondal, Himel
- Venue
- Indian Journal of Orthopaedics
- Published
- 2024-08-28
- Volume
- 58
- Issue
- 10
- Pages
- 1504-1505
- ISSN
- 0019-5413 (ISSN-L)
- 1998-3727
- PMC
- PMC11420400
- OpenAlex
- W4401942545
- Cited by
- 10
- References
- 3
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 2
- FWCI
- 0.77
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
- Locations
- Indian Journal of Orthopaedics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Education › Academic integrity and plagiarism
- Social Sciences › Library and Information Sciences › Academic Writing and Publishing
- Keywords
- generative AI, responsible AI use, research ethics, scientific writing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-11-13, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-13 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
