Fake science, a form of false information in science, slows scientific progress and undermines trust in science and the capacity of individuals and society to make evidence-informed choices, including life-or-death issues.1,2 Fake science produced from “paper mills” appeared in the biomedical literature around 2010 and poses issues with scientific integrity and credibility of research findings.3 Researchers are increasingly concerned that fake science is further exploited by Generative Artificial Intelligence (Gen AI) to generate fake text and altered images.4 The purpose of this perspective article is to discuss features of fake science related to the biomedical community, the role of Gen AI, and share resources and tools to help combat dissemination of false information.1,2
Background of Paper Mills
The term “paper mill” describes the process by which manufactured manuscripts are submitted to a journal for a fee on behalf of researchers with the purpose of providing an easy publication for them, or to offer authorship for sale.5 Originated from China, this malpractice was initially reported in Science in 2013.6 The term “paper mill” was first noted in a news report on Nature.7 Although the definition itself does not the imply the research is fraudulent, the vast majority of research published by paper mills is not trustworthy.8 The rise of paper mills has caused significant damage to trust in scientific literature.9 For example, Wiley (a global publisher) acquired Hindawi (an open-access publisher) in 2021 and discovered that Hindawi journals were flooded with articles from paper mills. Wiley retracted more than 11,300 Hindawi articles, shut down 19 journals, and discontinued the “Hindawi” name in 2024.10 Several recent studies found other examples illustrating the extent and scope of paper mill publications. A 2022 analysis over 53,000 journals from six publishers revealed that most journals reported 2–46% of suspect papers being submitted for review.8 In the same year, one research group used a software tool to flag publications in PubMed and found that 1% of publications contained text similar to publications from paper mills.11 Another analysis estimated that 1.5–2% of scientific papers published in 2022 were potential paper mill products, and the rate raised to 3% in biology and medicine papers.12 The editors of Naunyn-Schmiedeberg’s Archives of Pharmacology recently provided a detailed analysis of retracted papers in the journal, identifying many research integrity issues due to paper mills.13
The editorial office of Nature is planning to publish a large database on retracted scientific articles in the near future, and some preliminary analysis has been published recently.14 For additional readings about paper mills, please refer to two recent publications, and as well as a book chapter.3,15,16
Besides creating fake research papers, paper mills operate on other channels. Paper mills have registered many bizarre designs for medical devices with the UK’s Intellectual Property Office, and are actively selling “scientific inventorship” to many researchers in India.17 Some organizations sell paper authorships to non-US physicians to acquire the necessary publication records to apply for US residency positions.18 The Springer Nature journal, Cureus, closed six “academic channels” due to concerns mentioned above in November 2024.19 In November 2024, research integrity experts expressed concerns that paper mills might have infiltrated editorial boards of some journals, including the Springer Nature journal Scientific Reports.20
Entities related to paper mills also hijack authentic journals by creating clone websites.21,22 Innocent researchers may become victims of hijacked journals by submitting authentic papers to fake journals. For example, the now defunct “Springer Global Publication,” which is not affiliated with Springer Nature, has been reported to have hijacked journals from Elsevier, Springer Nature, and other major publishers.23
Common Features of Fake Papers and the Involvement of Gen AI
Over the years, scientific publishers and journal editors have identified several recurring features of fake papers from paper mills. Some of these ‘tells’ are unfortunately only visible during the peer review process, and not useful for the general readership. These features are summarized in Table 1.3,8,24 This table could be used as a checklist to screen for manuscript submissions, although it is by no means exhaustive. Some of these features are specific to certain types of data and will not be present in all manuscripts. In the following section, we will discuss a few in detail.
Table 1.
Common Features of Fake Papers
Manuscript Submission Level Features
|
| Content Level Features |
Method Level Features
|
Image Level Features
|
Fake papers may exhibit unusual co-author affiliations, as paper mills sometimes sell authorship of one manuscript to a group of academically unrelated individuals.27,28 Authors requesting to add or change authors after submission is also a common feature of fake papers.
Another major feature of fake papers is the existence of method and citation inaccuracies.3 Citations may also provide clues as to possible fake papers. Citations unrelated to the research, excessive self-citations, and multiple citations to the same journal, are examples of possible fake papers. Inconsistent methods are another hallmark of suspect papers such as using a rat cell line to study human genes, incorrect catalog numbers for reagents, incompatible primary and secondary antibodies, and so on. A investigation in 2024 reported that 235 cancer papers were based on data from seven non-existing cell lines.29 Another recent investigation reported unusually large rodents as a common questionable experimental parameter in paper mills.13
Image fraud is also prevalent in fake papers (Table 1). Journal editors should ask authors to provide raw images, especially when they have identified problematic images in the submissions, especially Western blot images. For readers who are not familiar with the technique, please refer to a blog article about Western blot.30
Fake Papers and Gen AI
Gen AI refers to technology that creates content—including text, images, video, and computer code—by identifying patterns in large quantities of training data and then creating original material that has similar characteristics.31 Gen AI tools are a growing trend in scientific publishing. Paper mills use Gen AI tools to help with content and image creation, with publishers also using Gen AI tools to identify potential fake papers or papers with plagiarized content.
AI-powered tools have been developed to detect replicated and altered images by human manipulation, including FigCheck (https://www.figcheck.com/imagecheck/), ImaCheck (https://www.imachek.com), ImageTwin (https://imagetwin.ai), and Proofig AI (https://www.proofig.com). These tools are gradually outperforming humans in finding manipulated images in papers.32 Unlike human-manipulated images, images generated by Gen AI have become so professional looking that they pose major challenges for detection. A recent news article in Nature reported that Proofig’s AI-image-detection tool achieved excellent results in detecting AI-generated images.33 Science-family journals started using Proofig AI to scan revised manuscripts at the beginning of 2024.34 During the 2025 Medical Library Association’s conference, the developers of Proofig AI reported that the software had achieved over less than 1% false negative rate identifying AI-generated cellular images, although it was not as good on AI-generated Western blotting images.
In 2021, research integrity experts reported a series of strange phrases were detected in hundreds of questionable articles between 2018 and 2021.35 These phrases were dubbed as “tortured phrases” and are suspected to be generated from automated software to disguise plagiarism. Examples of tortured phrases include: “top notch picture” instead of “high-resolution image,” “tainted people” instead of “infected individuals,” “blunder worth” instead of “error value.”25 The academic publishing sector quickly developed tools to detect AI-generated text, such as Turnitin’s AI detector (https://www.turnitin.com/solutions/topics/ai-writing/ai-detector/) and Compilatio’s AI Checker (https://www.compilatio.net/en/ai-detector-info/).36 GPT-2 Output Detector (https://openai-openai-detector.hf.space) is an example of an open-source software to detect AI generated text.
“Watermarking” technologies implemented by Gen AI developers may become a major force to spot fake papers prepared by Gen AI. These technologies can subtly modify the output of Gen AI, so that while “maintaining its intended meaning and appearance,” a watermark will “embed a hidden but consistent signal that serves as a verifiable marker of Gen AI origin.”37 For example, Google has implemented a watermarking theme, named “SynthID-Text” (https://huggingface.co/spaces/google/synthid-text/) in Gemini and Gemini Advanced chatbots.38 Fake papers prepared by Google chatbots can potentially be identified by a “SynthID-Text” theme detection tool. Interested readers can refer to a preprint for in depth discussion about watermarking techniques.39
Gen AI is also likely behind the current flood of letters-to-editors and editorial comments in several journals.40 More than half of the content published in 2024 by Neurosurgical Review were correspondence with more than 80% of the authors from a few countries. The Editor-in-Chief of Neurosurgical Review eventually decided to put a pause on letters-to the-editor submissions, citing that most of them appeared to be “driven by” advances in Gen AI tools such as ChatGPT.41 This development highlights the importance of developing automatic tools to detect text generated by Gen AI.
The widespread use of AI-assisted writing is posing a new challenge for detection of fake papers.42 Academ-AI (https://www.academ-ai.info/) is a project tracking the undeclared use of AI in the academic literature, which reported more than seven hundreds cases as of May 2025.43 Thus, it is vital for scientific journals to have clear guidelines to enforce disclosure of Gen AI usage. The AI Guidelines of the Journal of the American Medical Association is a good example, and is adopted by Missouri Medicine.44
Resources for Research Integrity
There are several major initiatives from stakeholders to combat paper mills. The resources and tools we complied with are listed in Table 2. We will briefly discuss them in the following section.
Table 2.
Resources and Tools to Combat Fake Science
The International Association of Scientific, Technical & Medical Publishers (STM) has several resources (https://stm-assoc.org/what-we-do/strategic-areas/research-integrity/), including the STM Integrity Hub, the Image Integrity Working Group, Content-update Signaling and Alerting Protocol and AI Resources. Another group dedicated to fighting fake publications is the Committee on Publication Ethics (COPE), which has a special focus on paper mills (https://publicationethics.org/cope-focus/cope-focus-paper-mills/). In 2023, COPE and STM jointly launched the United2Act project (https://united2act.org) to educate stakeholders on acting against paper mills. This project is currently seeking feedback from stakeholders on the draft resources.4
Websites, blogs, and commercial services dedicated to detecting papermills and fake papers are available. Retraction Watch (https://retractionwatch.com) is a blog that reports on retractions of scientific papers and investigates paper mill activities. The Retraction Watch Data can be either downloaded (https://gitlab.com/crossref/retraction-watch-data/) or searched from the web (https://retractiondatabase.org/RetractionSearch.aspx?). Retraction Watch maintains a Hijacked Journal Checker (https://retractionwatch.com/the-retraction-watch-hijacked-journal-checker/) as a database for hijacked journals. Lists of predatory publishers and journals can be found at the Predatory Journals website (predatoryjournals.org) and Beall’s List of Potential Predatory Journals and Publishers (https://beallslist.net/).45 Researchers can also use “Think, Check, Submit” (https://thinkchecksubmit.org) to review the quality of the journals they would like to submit to. PubPeer (https://pubpeer.com) is a website that allows users to discuss and review scientific research after publication. It serves as a whistleblowing platform for research integrity, as most comments are related to scientific misconduct, such as image manipulation, data manipulation and publishing fraud. The Problematic Paper Screener (https://www.irit.fr/~Guillaume.Cabanac/problematic-paper-screener/) reports papers with various issues related to fake science.46 As of May 2025, it reported more than 20,000 papers with tortured phrases. Science Integrity Digest (https://scienceintegritydigest.com) and For Better Science (https://forbetterscience.com) are blogs about scientific integrity, with many reports on paper mills.
Commercial services that track paper mills include: the Papermill Alarm from Clear Skies (https://clear-skies.co.uk), and Signals (https://research-signals.com). These groups utilize network-focused approaches to detect unusual patterns from authors, institutions, research topics, experimental design, and self-citation rates. Argos from Scitility (https://www.scitility.com/argos) provides a list of recent retractions, and analysis of authors, articles, and journals.
Commercial services that generate automatic research integrity reports for publishers and editors include the Integrity Manager from Morressier (https://www.morressier.com/products/research-integrity-manager/) and the Research Integrity Tool from River Valley Technology (https://rivervalley.io/products/research-integrity/). Dimensions Research Integrity (https://www.dimensions.ai/products/research-integrity/) claims to be the largest research integrity dataset and has developed “Trust Markers” at the individual level for each paper and the aggregated level for publishers and research institutions. Reviewer Zero AI (https://www.reviewerzero.ai/) is another commercial service in the developing phase to assist reviewers by checking manuscripts for features like missing citations, image manipulation and statistical inconsistencies. Reviewer Zero AI is also developing Author Verification Tool with a beta-version that is currently free to use (https://www.reviewerzero.ai/author-verification). RedacTek (https://redactek.com) is a free tool that identifies retracted, self-cited and PubPeer-flagged citations in an article to the third generation, which means the primary citations from the given article, the secondary citations in the primary citations, and the tertiary citations from the secondary citations.47 Connect Papers (https://www.connectedpapers.com/), and Litmap (https://www.litmaps.com/) offer free AI-powered tools that provides instantly visualization of an article’s references, and its relationship to related articles. Inciteful (https://inciteful.xyz/) is a similar free reference analysis tool without visualization.
Conclusion
Fake science produced by paper mills is evolving in the age of Gen AI and poses an increasing threat to research integrity and public trust in scientific information. While Gen AI allows paper mills to produce more sophisticated fake papers in larger quantities, the scientific community is also actively developing AI-based tools to detect fake science. Initiatives such as Black Spatula Project (https://the-black-spatula-project.github.io/) and YesNoError (https://yesnoerror.com/) are currently underway to develop AI tools to spot errors in research papers.48 Identifying and eliminating fake science is the responsibility of the entire scientific community.2,49 We hope that information presented in this article will equip researchers with knowledge, resources, and tools to identify fake papers, and combat fake science produced by paper mills in the age of Gen AI.
Footnotes
Xing Jian, PhD, (pictured), is Senior Support Scientist at the Bernard Becker Medical Library, School of Medicine, Washington University, St. Louis, Missouri, USA. Lauren H. Yaeger, MA, MLIS, is Clinical Librarian at the Bernard Becker Medical Library, School of Medicine, Washington University, St. Louis, Missouri, USA.
Disclosure: XJ is a reviewer for the NIH National Library of Medicine PubMed Central (PMC) Journal Review Program. The views expressed here are personal and do not necessarily reflect the views of the National Library of Medicine, the National Institutes of Health, or the US Department of Health and Human Services. Artificial intelligence was not used in the study, research, preparation, or writing of this manuscript.
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