Most major publishers now have artificial intelligence (AI) policies, but they are a mess of contradictions. One journal demands every prompt and software version; another just says "be honest." This inconsistency leaves scientists in a grey zone, unsure if using spell-checker AI crosses an ethical line. Worse, large language models are notorious "hallucinators," confidently generating plausible but completely fake citations or scientific nonsense. There is also the privacy nightmare: feeding unpublished patient data into a public chatbot. Because of these risks, a clear, unified, and practical framework is urgently needed to guide researchers without punishing them for using modern tools.
Researchers led by experts from The First Affiliated Hospital of Shenzhen University, China, working with a multidisciplinary international panel that included contributors from universities, hospitals, and editorial institutions in China, Italy, Japan, Canada, Australia, and other countries, publishes (DOI: 10.1016/j.rerere.2026.02.001)1 the new guidance on March 10, 2026, in the journal Regenesis Repair Rehabilitation. The team first audited AI rules from 15 major publishers, including Elsevier and the JAMA Network. They found broad agreement on principles but chaos in practice. Using a structured Delphi process, they built a consensus that fits real-world research workflows, resulting in a simple, section-by-section checklist for authors to disclose exactly how they used AI.
The new rules draw a hard line in the sand. AI can fix your grammar, but it cannot touch your data. The guidelines strictly prohibit using generative AI to create or manipulate primary research images, including microscopy, gels, and flow cytometry plots. For references, the message is clear: do not let AI generate them. The tools are too prone to fabricating authors, journal names, and DOIs. However, AI gets a green light for supportive tasks, like summarizing a discussion or refining an abstract, as long as a human verifies every claim.
A standout feature is the section-specific guidance. In the Methods section, AI can improve readability but cannot invent missing steps. In the Results section, the text can be polished, but the numbers themselves must come from genuine experiments. For non-data visuals, like workflow diagrams, AI is allowed but must be fully disclosed. The authors even put their own advice into practice, using Gemini and ChatGPT during writing—but only for language and structure, never for core science. The final takeaway is simple: AI is a powerful assistant, but the human author remains the sole, accountable author.
"We are not the AI police, and we are not telling people to throw away their laptops."
Affiliated Hospital of Shenzhen University, China, Author
"The worst outcome would be researchers secretly using AI anyway, afraid to admit it," the authors said. They explained that the real danger is not AI itself, but invisibility. "If a chatbot rewrites your discussion or suggests a citation, that is fine—just put it in a table. The moment you claim AI-generated text as your own original thought, or a fabricated reference as real, you have crossed into misconduct." They added: "Transparency turns a black box into a simple tool. And that is all this should be."
For researchers, the consensus removes guesswork. They now have a checklist they can hand to collaborators or include in supplementary files. For journals, the standard offers a ready-made audit trail to streamline peer review. The guidelines are especially valuable for non-native English speakers, who can confidently use AI for language help without ethical fear. As AI evolves, the authors commit to updating the rules regularly. Ultimately, this work shifts the conversation from "is AI cheating?" to "how do we use AI well?" — protecting science from silent errors while embracing the efficiency that modern writing tools provide.
Reference:
1) http://sciencedirect.com/science/article/pii/S2950575526000067?via%3Dihub
(Newswise/HG)
