GoKawiilIn early 2024, a startling illustration in a published paper sparked spirited debate on social media. The image showed a rat endowed with a penis and testicles that were bigger than the rest of the animal’s body. The authors noted that the illustration was generated by an artificial-intelligence model called Midjourney, but it was obviously inaccurate, depicting four testes and including strange, misspelt text such as ‘sserotgomar cells’. Somehow, it passed peer review.
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“This was the first mainstream example of where an AI-generated image made it into a scientific paper — and it shouldn’t have been published,” says Elisabeth Bik, a science-integrity consultant in San Francisco, California, who wrote about the incident on her blog. AI tools at the time were not good enough to create credible user-prompted illustrations, as the rat figure showed — but that was then. Two years later, “AI is much better and continuously improving, and we’re at a point where we can no longer distinguish fake from real,” says Bik.
Still, errors continue to crop up. In April, a study by researchers in China was retracted by the New England Journal of Medicine because of image manipulation. The numbers on a tape measure, displayed at the top of the figure, were incorrect, exposing the use of an AI tool. In a comment on the post-publication discussion forum, PubPeer, one of the authors notes that they had used an AI tool to adjust the placement of the tape measure, which had been improperly positioned during an emergency medical procedure. “The irregular numbering is an unintended artifact from this adjustment,” they wrote.
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Graphics, which include schematics, data figures and diagrams, are a crucial part of scientific publishing. And they can substantially affect an article’s influence: an analysis of eight million graphics published in scientific papers found “a significant correlation between scientific impact and the use of visual information, where higher impact papers tend to include more diagrams” (P.-S. Lee et al. IEEE Trans. Big Data 4, 117–129; 2018). However, many researchers have neither the resources nor the skills to create aesthetically pleasing and informative images themselves.
The potential for modern AI systems to assist researchers in generating graphics is “huge”, says Sebastian Porsdam Mann, an ethicist at the Centre for Advanced Studies in Bioscience Innovation Law at the University of Copenhagen. AI tools can make scientific illustration accessible to everyone, allowing researchers to better communicate their science in a fraction of the time and at a lower cost than ever before, he says. And it is in researchers’ interests to have good papers that explain complex topics, with good data visualizations and graphical abstracts, he adds.
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But, as with text, AI image generators such as Midjourney and OpenAI’s DALL-E still make mistakes — the effects of which can range from personal embarrassment to professional censure. “It’s still early days and these are still largely untested waters,” says Mann. But if there’s the slightest hint that you’ve done something wrong while using AI tools, “journals will probably take that very seriously right now”.
Here are some guidelines to help researchers navigate this rapidly changing landscape.
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