Most of the debate about generative AI in research has concerned the written word: what authors must declare when they use AI tools to help draft a manuscript. But generative AI does not only produce text. It produces images — and images in a scientific paper occupy a fundamentally different place from prose. A figure is often presented as evidence: a micrograph, a gel, a scan, a chart of results is taken to be a faithful record of something that was observed or measured. When such an image can be conjured by a model that has never observed anything, the most basic assumption of scientific communication — that what you are shown is real — comes under threat. This is why AI-generated images raise concerns that are sharper, and in some respects more dangerous, than those raised by AI-generated text. The question belongs within the generative-AI disclosure domain of the CASRAI Dictionary.
Why images are different from text
The crucial difference is the relationship between an image and reality. Text in a paper is understood to be the authors’ account, their argument and interpretation; readers know it is written. But many images in scientific papers are understood to be records of fact — this is what the cells looked like, this is the structure that was resolved, this is the experimental output. The integrity of the entire scientific record depends on that understanding holding true. A generative model can now produce an image that looks exactly like such a record but corresponds to nothing that was ever observed. In the context of a results figure, that is not illustration; it is fabrication, which has always been among the gravest forms of research misconduct. The danger is not hypothetical or cosmetic: a fabricated figure can mislead readers, reviewers and an entire field into believing in findings that do not exist.
The integrity and manipulation concerns
Several specific concerns flow from this:
- Fabrication of results. The starkest risk is the generation of fake data visualisations, fake imaging results or fake experimental outputs presented as genuine — falsification of the scientific record itself.
- Undisclosed manipulation. AI tools that retouch, enhance or alter genuine images can cross the long-standing line between acceptable adjustment and impermissible manipulation, especially when done invisibly.
- Erosion of trust. If readers can no longer assume that scientific images are authentic, the evidential value of figures — central to how science is communicated and checked — is undermined across the board.
- Detection difficulty. Synthetic images can be very hard to distinguish from real ones, which means the safeguards cannot rely on catching fakes after the fact and must lean heavily on honesty and clear rules.
These concerns sit on top of a pre-existing problem: image integrity, including inappropriate duplication and manipulation of figures, was already one of the most common sources of research-integrity cases before generative AI arrived. The new tools pour fuel on a fire already burning.
Why many publishers ban AI-generated figures
Faced with this, a notable number of publishers and journals have taken a stricter line on images than on text. Where policies on AI-assisted writing typically permit it subject to disclosure, policies on AI-generated images frequently prohibit them in scientific figures outright, particularly where a figure represents data or results. The reasoning follows directly from the difference described above. An AI tool that helps phrase a sentence does not pretend the sentence is an observation; an AI-generated results figure, by its nature, presents as real something that is not. Because the risk is fabrication of evidence rather than mere stylistic assistance, the proportionate response is often a ban rather than disclosure. A common pattern in publisher policy is therefore a near-prohibition on generative AI for figures that depict data, alongside more permissive, disclosure-based treatment of AI used for purely decorative or schematic illustrations that no one would mistake for evidence — and even then, transparency is expected.
The disclosure requirement
Where AI-generated or AI-assisted imagery is permitted at all — for example, a clearly labelled conceptual illustration, a graphical abstract, or a schematic — disclosure becomes the governing requirement, as it is for text. The emerging expectations include several elements:
- Declare any use. Authors should state where and how generative AI was used to create or alter images, so that readers and editors are not misled about what they are looking at.
- Never pass off generated content as real data. The bright line is that no AI-generated image may be presented as an authentic record of an observation or result.
- Preserve and provide originals. Authors are increasingly expected to retain unaltered original images and supply them on request, so that genuine figures can be verified.
- Follow the specific journal’s policy. Because rules differ between venues, authors must check and comply with the policy of the journal they are submitting to.
COPE and the integrity bodies
Editorial and integrity organisations have shaped this developing consensus. The Committee on Publication Ethics (COPE) and publishers’ own guidance consistently anchor the discussion in long-standing principles of image integrity — that figures must faithfully represent the work, that manipulation which misleads is misconduct, and that AI does not change these duties but makes them more pressing. The framing is instructive: generative AI has not created a new category of wrongdoing so much as made an old and serious one — fabricating or manipulating the visual evidence in a paper — far easier to commit and harder to detect. The policy response is a reassertion of the principle that scientific images must be honest, adapted to a world in which dishonest images are trivially easy to make. These questions connect closely to the responsibilities discussed in our material on authorship and accountability.
A consistent vocabulary for disclosure
For disclosure of AI use in images to be meaningful across journals, repositories and integrity systems, what is disclosed must be described consistently — what tool was used, on which figures, for what purpose, and whether the result is illustrative or evidential. That consistency is what the CASRAI Dictionary works towards: a shared vocabulary so that a statement about AI use in a paper’s figures is understood the same way wherever it is recorded. And because creating genuine figures and visualisations is real research contribution, that work can be described using the same framework as any other — the CRediT taxonomy and its full set of contribution roles, with Visualization recognising the creation of legitimate data presentation. Generative AI can make a convincing picture of anything; the durable principle — that the images in a scientific paper must be truthful representations of real work — is exactly what disclosure and these firmer rules exist to defend.
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