J Med Internet Resjmir1Journal of Medical Internet ResearchJ Med Internet Res1438-8871JMIR PublicationsToronto, Canadav28i1e10493410.2196/104934News and PerspectivesIdentity Theft in Academia: Predatory Journals, AI, and the Rise of False AuthorshipDominyCliffJMIR CorrespondentCleggKayleigh-Ann2026256202628e1049341706202617062026© JMIR Publications. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.6.2026. 2026Fraudulent papers are on the rise in scientific publishing. In this News and Perspectives article, JMIR Correspondent Cliff Dominy reports on recent cases of false authorship and how they might be prevented to safeguard trust in science.
publishingauthorshipscientific misconductpeer reviewartificial intelligenceopen scienceretraction of publication as topiceditorial policiesKey Takeaways:
Predatory journals are stealing the identities of reputable scientists in false authorship scams to improve their credibility with prospective clients.
Recent high-profile cases have caused reputational harm to authors and editors and have exposed weaknesses in our trust-based academic system.
Prepublication screening of academic manuscripts, along with the reform of academic incentives, will probably be central components of an effective response.
One of the interview sources in this article, Nathaniel Gore, is an employee of JMIR Publications Inc. No financial or other potential conflicts of interest exist with the author.
One afternoon in September 2024, Diomidis Spinellis, a Professor of Software Engineering at the Athens University of Economics and Business, opened his inbox. An email from a colleague stood out. Would he be able to write a brief article for the departmental newsletter covering his recent paper, “Global Business Strategies in the Digital Age?” Spinellis, a prominent academic, did not recall writing it, so he opened the hyperlink to refresh his memory. To his disbelief, he found his name on a paper that wasn’t his, in a journal he hadn’t heard of.
The journal picked the wrong guy to impersonate; Spinellis is an expert in mining data repositories, and he got straight to work. The Global International Journal of Innovative Research (GIJIR) was relatively new, with just two volumes containing 220 academic papers. Further research picked up some red flags, showing that many of his fellow GIJIR “authors” were well known in their fields, but likely also impersonated—some had already passed away at the time of their supposed publications. Worst of all, the fake paper had attracted three citations in the months since its publication.
It got worse; the quality of the writing was, in Spinellis’s opinion, “trash.” He ran a few papers through an AI detector, usually reserved for his students’ essays. The result: generative AI was responsible for up to 100% of each paper. Spinellis dissected the GIJIR dataset and published his findings in a case study. He found their papers fell into three broad categories.
False authorship papers: the predominant group, typically AI-generated by the journal, with unsuspecting scholars’ names used to boost the journal’s credibility.
Bought authorship papers: often AI-generated, either by unscrupulous authors or as a service by journals for their clients.
Genuine papers (the minority): real research by real scientists unaware of or unconcerned about the journal’s practices.
False authorship is not new, but thanks to AI it is increasingly easy to produce. In May 2025, members of the International Committee of Medical Journal Editors (ICMJE) recognized the growing problem and alerted the scientific community to the strategies used by predatory journals, which included the following:
Mimicry of established journal names and branding
False claims of membership of the Committee on Publication Ethics, Council of Science Editors, or ICMJE
Fabricated indexing and citation metrics
Prominent scientists as authors and editors without their knowledge or consent
False authorship papers are an advertising tool used by predatory journals to attract unscrupulous scientists to the brand. These journals will “assume the identities of [established scientists] to confer legitimacy on themselves,” says Reese Richardson, PhD, a bioinformatician at Northwestern University in Illinois, who has tracked and continues to track predatory publishers.
The Topol IncidentIn April 2026, cardiologist, author, and social media icon Eric Topol, MD, discovered his name had been used in a false-authorship scam. With over one million followers across several social media channels, Topol was alerted to a paper titled “Implementation Science for AI Integration in Digital Health Systems” in the Journal of Digital Health Implementation. Within hours of him sounding the alarm, the paper, journal, and publisher had disappeared. An investigation by the science watchdog Retraction Watch revealed that the fraudsters had followed the predatory playbook to the letter. Their big mistake—using the name of a leading digital health figure in the ruse, which dramatically reduced its half-life in the scientific record.
It is not only the authors who suffer reputational harm in these schemes. The supposed “editors” of the paper—themselves impersonated by the journal—unwittingly got involved in the scandal. Zarnie Khadjesari, PhD, a professor of Health Science at the University of East Anglia, received a message from fellow “editor” Angelo Rossi-Mori, PhD, alerting her to the fake paper.
Both well-respected scientists were, in Khadjesari’s words, “angered and disturbed” by the deception. To compound matters, they had been mistakenly and very publicly identified as complicit in the fraud—which had been circulated to Topol’s more than 1 million followers. For Khadjesari, this added insult to injury, an experience she describes as “exhausting and emotional,” and she took to social media to defend herself.
Stopping Predatory JournalsDetection: Sooner, Not LaterCan technology itself tackle the AI-accelerated fake authorship problem? Rossi-Mori, former associate researcher at the Italian National Research Council, states via email, “This is a serious problem, but not a completely new one.” He blames generative AI and “the rise of social media, [for] the speed, scale, and apparent credibility with which such false material can now circulate.”
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Screening for false science must happen prepublication; like toothpaste, it’s better managed while still in the tube. An effective screening system would help researchers flag predatory journals and journal editors flag fraudulent papers before they can burden the peer-review system:
Validating the authenticity of the journal: Is the journal’s identifier (ISSN) legitimate, is the digital object identifier (doi) address valid, and has the paper been independently indexed in PubMed or Google Scholar? Think.Check.Submit.org provides a checklist for researchers doing due diligence on unfamiliar publications.
Validating the authenticity of the author: False authorship involves both real and imaginary people, and establishing an independent registry of verified scientists with valid affiliations would expose bogus science before it can enter the scientific record.
To address the identity issue, Nathaniel Gore, Product Development & Partnerships Director at JMIR Publications, is “building a better infrastructure” with opensci.id. The independent opensci.id registry aims to centralize academic identities in an online database linking valid researchers with their affiliations and body of work. If the authors on a manuscript don’t match the academics in the registry, the paper can be flagged for extra scrutiny. For Gore, it’s about trust. “We’ve built an entire system on claimed rather than verified identities,” he notes, “We’re heavily reliant on trust signals that are too easy to fake.”
Reinventing Scientific SuccessThese proposed measures are important first steps in preserving the integrity of science. They will make it harder for predatory journals to operate, but in the race between technology and tech-detectors, will they be enough?
At the core of the problem is the publish-or-perish incentive system by which all academics are measured. Richardson feels that “fixing it requires rebuilding the system of incentives that created this problem from the start.” His concern is “can AI-detectors perform effectively at their job,” considering that “the threat is shifting all the time?”
The problem of false authorship is complex and growing, and effective solutions will be expensive. Publishers will need to invest staff and capital to ensure that AI-based screening steps are both accurate and equitable, with a human at the wheel. Ultimately, safeguarding science—and public trust in science—will require the vigilance of the whole scientific community.