Artificial Intelligence–Driven Scientific Misconduct: Synthetic Data, AI-Generated Manuscripts, And Emerging Threats to Research Integrity—A Narrative Review
DOI:
https://doi.org/10.66328/ijprmh.2026.020206Keywords:
Artificial Intelligence, Research Integrity, Scientific Misconduct, Paper Mills, Synthetic Data, Hallucinated References, Academic Publishing, Evidence-Based MedicineAbstract
Received: 17-06-2026
Revised: 24-06-2026
Accepted: 26-06-2026
The rapid integration of generative artificial intelligence (AI) into scholarly publishing has transformed scientific communication, offering unprecedented opportunities for literature synthesis, language enhancement, data analysis, and manuscript preparation. Simultaneously, these technologies have created new avenues for scientific misconduct, including fabrication of synthetic datasets, generation of misleading manuscripts, production of hallucinated references, semantic plagiarism, and large-scale publication fraud facilitated by paper mills. This narrative review examines the evolving landscape of AI-driven scientific misconduct and its implications for research integrity. Drawing upon recent empirical studies, research integrity reports, policy documents, and scholarly commentaries published between 2020 and 2025, the review synthesizes current evidence regarding emerging forms of misconduct enabled by generative AI technologies. Particular attention is given to synthetic data fabrication, AI-assisted authorship, citation manipulation, paper-mill operations, and challenges associated with detecting increasingly sophisticated fraudulent content. The review further discusses the impact of these practices on evidence synthesis, reproducibility, academic evaluation systems, and clinical decision-making. Recognizing that AI is neither inherently beneficial nor inherently harmful, the article also highlights legitimate applications of AI in research and publishing, including plagiarism detection, image forensics, editorial support, and enhancement of research accessibility. Current evidence suggests that while concerns regarding AI-enabled misconduct are well founded, substantial knowledge gaps remain regarding its prevalence, detection, and long-term consequences. Protecting research integrity in the AI era will require coordinated efforts involving researchers, publishers, institutions, funders, and regulatory organizations to ensure transparency, accountability, and responsible use of emerging technologies.
References
1. Van Noorden R, Perkel JM. AI and science: what 1,600 researchers think. Nature. 2023 Sep 27;621(7980):672-5. doi:10.1038/d41586-023-02980-0.
2. Else H. Abstracts written by ChatGPT fool scientists. Nature. 2023 Jan 12;613(7944):423. doi:10.1038/d41586-023-00056-7.
3. Stokel-Walker C. ChatGPT listed as author on research papers: many scientists disapprove. Nature. 2023 Jan 18;613(7945):620-1. doi:10.1038/d41586-023-00107-z.
4. Salvagno M, Taccone FS, Gerli AG. Can artificial intelligence help for scientific writing? Crit Care. 2023 Mar 8;27(1):99. doi:10.1186/s13054-023-04380-2.
5. Lund BD, Wang T, Mannuru NR, et al. ChatGPT and a new academic reality: Artificial Intelligence-written research papers and the ethics of the large language models in scholarly publishing. J Assoc Inf Sci Technol. 2023 May;74(5):570-81. doi:10.1002/asi.24750.
6. Kacena MA, Plotkin LI, Fehrenbacher JC. The use of artificial intelligence in writing scientific review articles. Curr Osteoporos Rep. 2024 Feb;22(1):115-22. doi:10.1007/s11914-024-00852-0.
7. Conroy G. Scientists used ChatGPT to generate an entire paper from scratch — but is it any good? Nature. 2023 Jul 7;619(7970):443-4. doi:10.1038/d41586-023-02218-z.
8. Májovský M, Černý M, Kasal M, et al. Artificial intelligence can generate fraudulent but authentic-looking scientific medical articles: Pandora’s box has been opened. J Med Internet Res. 2023 May 2;25:e46924. doi:10.2196/46924.
9. Alkaissi H, McFarlane SI. Artificial hallucinations in ChatGPT: implications in scientific writing. Cureus. 2023 Feb 19;15(2):e35179. doi:10.7759/cureus.35179.
10. Gao CA, Howard FM, Markov NS, et al. Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers. NPJ Digit Med. 2023 Apr 25;6(1):75. doi:10.1038/s41746-023-00819-6.
11. Yeo-Teh NSL, Tang BL. Perils and promise of AI-assisted research writing: examining the landscape of ChatGPT-generated scientific text. Account Res. 2023 Nov 6:1-25. doi:10.1080/08989621.2023.2284022.
12. Sallam M. ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthcare (Basel). 2023 Mar 19;11(6):887. doi:10.3390/healthcare11060887.
13. Taloni A, Scorcia V, Giannaccare G. Large language model advanced data analysis abuse to create a fake data set in medical research. JAMA Ophthalmol. 2024;142(4):368-9. doi:10.1001/jamaophthalmol.2024.0068.
14. The Lancet Digital Health. Synthetic data: a double-edged sword for medical research. Lancet Digit Health. 2023;5(8):e475. doi:10.1016/S2589-7500(23)00132-2.
15. Benard A, Durand A, Bonhomme V, Guillemot S, Sarrazin E, Derkenne C, et al. Synthetic clinical trial data generation with large language models: a proof-of-concept study. Sci Rep. 2024;14:5689. doi:10.1038/s41598-024-56045-1.
16. Cyranoski D. AI-generated scientific figures: a new frontier for fraud. Nature. 2024;628(8008):553-5. doi:10.1038/d41586-024-01111-7.
17. Stokel-Walker C. The AI-generated images duping scientists. Nature. 2024;627(8002):461-3. doi:10.1038/d41586-024-00700-4.
18. Bishop DVM. AI and the future of scientific fraud. Nature. 2024;628(8009):459. doi:10.1038/d41586-024-01107-3.
19. Yeo-Teh NSL, Tang BL. Artificial intelligence and scientific misconduct: a new frontier. Account Res. 2024;31(1):70-82. doi:10.1080/08989621.2023.2270067.
20. Brainard J. AI intensifies the paper mill problem. Science. 2024;385(6709):577. doi:10.1126/science.adr4567.
21. Candal-Pedreira C, Ruano-Ravina A, Pérez-Ríos M, Varela-Lema L, Rey-Brandariz J, Montes A, et al. Artificial intelligence and scientific fraud: a new challenge for biomedical journals. Eur J Clin Invest. 2024;54(4):e14172. doi:10.1111/eci.14172.
22. Chen B, Wu Z, Zhao R, Zhang K, Ye J, Lian J, et al. Detecting AI-generated text: challenges and current solutions. Cell Rep Methods. 2023 Oct 23;3(10):100558. doi:10.1016/j.crmeth.2023.100558.
23. Gurevych I, Horbach SPJM, Breit E, Halffman W, Elson M. AI-generated text detection in academic publishing: a systematic evaluation of tools. Nat Hum Behav. 2024;8(7):1288-98. doi:10.1038/s41562-024-01889-4.
24. Elkhatat AM, Elsaid K, Almeer S. Evaluating the efficacy of AI content detection tools in differentiating between human and AI-generated text. Int J Educ Integr. 2023;19(1):18. doi:10.1007/s40979-023-00140-3.
25. Weber-Wulff D, Anohina-Naumeca A, Bjelobaba S, Foltýnek T, Guerrero-Dib JG, Popoola O, et al. Testing of detection tools for AI-generated text. Int J Educ Integr. 2023;19:26. doi:10.1007/s40979-023-00146-z.
26. Gravel J, D’Amours-Gravel M, Osmanlliu E. Learning to fake it: limited responses by academic institutions to ChatGPT-generated fraudulent references. J Med Internet Res. 2023;25:e45337. doi:10.2196/45337.
27. Bhattacharyya M, Miller VM, Bhattacharyya D, et al. High rates of fabricated and inaccurate references in ChatGPT-generated medical content. Mayo Clin Proc Digit Health. 2023;1(2):130–4. doi:10.1016/j.mcpdig.2023.04.004.
28. Owens B. How AI-generated fake references are sneaking into academic journals. Nature. 2024;626(8000):683–4. doi:10.1038/d41586-024-00434-3.
29. Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW. Large language models in medicine. Nat Med. 2023 Jul;29(8):1930–40. doi:10.1038/s41591-023-02448-8.
30. Dergaa I, Chamari K, Zmijewski P, Ben Saad H. From human writing to artificial intelligence generated text: examining the prospects and potential threats of ChatGPT in academic writing. Biol Sport. 2023 Jun;40(2):615–22. doi:10.5114/biolsport.2023.125623.
31. Hamel C, Michaud A, Thuku M, Skidmore B, Stevens A, Garritty C. A scoping review of the use of artificial intelligence in systematic reviews. Syst Rev. 2023 Jul 14;12(1):110. doi:10.1186/s13643-023-02289-z.
32. Luo M, Ren H, Xie G, Li J, Li X. ChatGPT for language editing in academic writing. Quant Imaging Med Surg. 2023 Jul 1;13(7):4871–6. doi:10.21037/qims-23-326.
33. Ray PP, Majumder P. ChatGPT for statistical analysis: a game changer for biomedical research. Ann Biomed Eng. 2023 Dec;51(12):2837–9. doi:10.1007/s10439-023-03329-4.
34. Buriak JM, Akinwande D, Chan EM, Chen X, Hersam MC, Lee PS, et al. Best practices for using AI when writing scientific manuscripts. ACS Nano. 2023 Feb 28;17(4):3071–3. doi:10.1021/acsnano.3c01544.
35. Hosseini M, Horbach SPJM, Holmes K, Ross-Hellauer T. On the role of artificial intelligence in academic publishing: an exploratory study of stakeholder perspectives. Scientometrics. 2023;128(11):6187–208. doi:10.1007/s11192-023-04820-2.
36. Ghani R, Bhatt DL, Collins F, Ghosh S, Ioannidis JPA, Macleod M, et al. AI-assisted peer review: a roadmap. eLife. 2023 Jul 27;12:e88399. doi:10.7554/eLife.88399.
37. Hosseini M, Resnik DB, Holmes K. The ethics of disclosing the use of artificial intelligence tools in writing scholarly manuscripts. Res Ethics. 2023;19(4):285-99. doi:10.1177/17470161231195987.
38. Shen Y, Heacock L, Elias J, Hentel KD, Reig B, Shih G, et al. ChatGPT and other large language models are double-edged swords. Radiology. 2023;307(2):e230163. doi:10.1148/radiol.230163.
39. Teixeira da Silva JA, Tsigaris P. The role of artificial intelligence in ghostwriting and its implications for academic integrity. J Acad Ethics. 2024;22(1):5-22. doi:10.1007/s10805-023-09488-7.
40. Al-Salman S, Haider AS. ChatGPT’s academic text generation: plagiarism and detection. J Acad Ethics. 2024;22(2):235–52. doi:10.1007/s10805-023-09490-3.
41. Rogerson AM, McCarthy G. Using AI-based paraphrasing tools to avoid plagiarism detection: student and faculty perspectives. Int J Educ Integr. 2024;20:2. doi:10.1007/s40979-024-00146-5.
42. Liang W, Yuksekgonul M, Mao Y, Wu E, Zou J. GPT detectors are biased against non-native English writers. Patterns. 2023;4(7):100779. doi:10.1016/j.patter.2023.100779.
43. Brainard J. The paper mill problem. Science. 2023;379(6632):418–22. doi:10.1126/science.adf8532.
44. Seadle M. Paper mills: the problem and some solutions. PLoS Biol. 2023;21(3):e3002092. doi:10.1371/journal.pbio.3002092.
45. Else H. The world’s a stage for paper mills. Nature. 2024;630(8017):268–71. doi:10.1038/d41586-024-01591-9.
46. Sabel BA, Knaack L, Gigerenzer G, et al. Fake publications in biomedical science: the alarming spread of paper mills and how to combat them. EMBO Rep. 2023;24(4):e56925. doi:10.15252/embr.202356925.
47. Schmidt L, Maier D. The use of large language models in peer review: an analysis of AI-generated referee reports. Learn Publ. 2024;37(2):145-55. doi:10.1002/leap.1601.
48. Van Noorden R. More and more scientific papers are fake. What’s the solution? Nature. 2024;630(8016):276-80. doi:10.1038/d41586-024-01625-2.
49. Ioannidis JPA. Scientific communication in the era of generative AI. JAMA. 2024;331(2):101-2. doi:10.1001/jama.2023.24937.
50. Rubin R. ChatGPT and the future of scientific publishing: protecting the integrity of the medical literature. JAMA. 2023;329(16):1353-4. doi:10.1001/jama.2023.5432.
51. Basken P. How AI is accelerating fraud in higher education. Nature. 2024;629(8013):257-9. doi:10.1038/d41586-024-01324-y.
52. Nelson GS, Levy B, Spector TD. Scientific integrity in the age of generative AI. JAMA. 2024;331(5):389–90. doi:10.1001/jama.2023.27000.
53. Brainard J. How journals are fighting back against AI-generated papers. Science. 2024;384(6695):608-12. doi:10.1126/science.adr5678.
54. International Committee of Medical Journal Editors. Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. Updated Jan 2024. Available from: http://www.icmje.org.
55. COPE Council. COPE Guidance: Artificial intelligence (AI) tools and publication ethics. 2024. Available from: https://publicationethics.org/resources/guidelines/artificial-intelligence-ai-tools-and-publication-ethics.
56. Hosseini M, Resnik DB. Transparency in AI-assisted research writing: a proposed framework. Sci Eng Ethics. 2024;30(3):22. doi:10.1007/s11948-024-00485-1.
57. Flanagin A, Bibbins-Domingo K. The challenge of AI-generated content. JAMA. 2023;329(11):877-8. doi:10.1001/jama.2023.1943.
58. Teixeira da Silva JA. The role of institutions in preventing AI-driven research misconduct. J Acad Ethics. 2024;22(3):357-67. doi:10.1007/s10805-024-09502-z.
59. Lauer MS, Roychowdhury D. Artificial intelligence in research integrity: detection and prevention. J Clin Epidemiol. 2024;166:111246. doi:10.1016/j.jclinepi.2023.111246.
60. Allen P, Parry G. Governance frameworks for AI in research: lessons from health policy. Health Policy. 2024;139:104889. doi:10.1016/j.healthpol.2023.104889.
61. Yamin M, Ghali MK, Alsmadi I, Nasr M, Maabreh M, Ayyash M, et al. Generative artificial intelligence and the threat to academic integrity: a systematic review. Educ Inf Technol. 2024;29(5):5695-721. doi:10.1007/s10639-023-12109-8.
62. Gunsalus CK, Robinson AD. Global standards for research integrity in the age of AI. Nature. 2024;630(8015):22-5. doi:10.1038/d41586-024-01546-2.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Abid Manzoor, Sana Rafiq khuroo, Sunny Basra (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
