The Next Frontier: AI-Generated Images Undermining Trust in Science
A photograph of Earth glowing in deep space, the Moon’s cratered horizon stretching across its foreground, caught many people’s eyes in April 2026. Astronauts captured the image while aboard NASA
Aphotograph of Earthglowing in deep space, the Moon’s cratered horizon stretching across its foreground, caught many people’s eyes in April 2026. Astronauts captured the image while aboard NASA’s Artemis II mission, and like the famousApollo 8 “Earthrise” image, the picture felt instantly real and inspiring for many. But when almost anyone canfabricate a visually similar imagein seconds from a text prompt using artificial intelligence, how do people decide which image is real?The proliferation of AI-generated science images in public spaces is not simply a misinformation problem. As a researcher who studiesvisual science communication and public trust, I believe it also contributes to acrisis of trust in science in the age of AI, and the tools scientists have long relied on to establish visual credibility are losing their grip. AI-generated images infiltrate scienceAI tools are already changing how scientific visuals arecreated, shared and publicized. Researchers use them togenerate illustrations,create synthetic data,edit lab imagesandproduce materials for education and public outreach. While AI can help scientists communicate complicated ideas morecreatively and efficiently, these same toolsblur the linesbetween illustration, enhancement and fabrication. In 2024, two papers were retracted after publishingAI-generated figures posessingbiologically impossible structures. In April 2026, the New England Journal of Medicine retracted a paper after discovering that aclinical image had been manipulated with AI. These are just cases that came to mass public attention and are likely just the tip of the iceberg. Researchers have warned thatAI-generated visuals pose growing threatsin fields that depend heavily on visual evidence, such as materials science. Academic publishers are beginning toadopt AI-detection tools. However, systems designed to detect fake images willalmost always lag behindsystems designed to create them. Many detectors can identify only image patterns they were trained to recognize. As new AI models emerge, developers must constantly obtain new data and retrain detectors to catch up. The biggest concern are realistic-looking visuals that subtlydistort scientific details while remaining believableenough to pass initial review. Trust in scientific imagesFor decades, scientific images carried authority partly because they weredifficult to produce. Creating microscope images, climate graphs and space photographs required expensive equipment, institutional resources and specialized expertise. Most people assumed such images represented true observations because very few people could make them. Research in science communication, including my own, suggests that people judge scientific visuals using a few mental shortcuts. Does the imagelook technically sophisticated? Does itcome from a trusted institution? Does itmatch what I already believe? Generative AI is undermining all three of these heuristics, or mental shortcuts. Today, anyone can create a polished, scientific-looking image from a text prompt. Images are alsodetached from their original sourcewhen circulating online. When visual quality and institutional attribution become unreliable cues for judging the credibility of science images, people tend to fall back on something else:their own prior beliefs. As a result, authentic scientific images that challenge someone’s existing beliefs can now be dismissed as AI-generated, whereas fabricated images that confirm them are easily accepted as evidence. AI, in this way, mayamplify motivated reasoning– that is, people’s tendency to accept what they already agree with and question what they do not. This shift matters because visuals have long served asevidence for scientific claims. Nonexpert audiences rely on images not only to see what scientists have discovered but also todevelop an emotional connectionandperceive credibilityin the science being presented. If audiences stop trusting visual evidence altogether, science loses one of its most powerful tools for public communication. Transparency, not restrictionAI tools offer real benefits for researchers communicating their work to diverse audiences. The challenge is using these tools without quietly transferringAI’s credibility deficitonto the science the images are meant to convey. One practical path forward is for researchers to treatimage provenance– where an image came from and how it was created – with the same seriousness they already apply to data provenance. Scientists routinely disclosefunding resources, study methodologies and conflicts of interest. Similar standardsmay now be necessary for scientific images. Was AI used to generate or modify this image? Is it a direct observation, a simulation or an illustration? What exactly does the image represent, and how was it verified? Can it be replicated by other researchers? https://www. youtube. com/embed/Bj8IAoTnyNw?wmode=transparent&start=0 A particularly inaccurate scientific image of a rat that was published in a journal went viral. My colleagues and I found that people’sfamiliarity with AI significantly shapeshow they judge the credibility of AI-generated visuals. Those familiar with AI tools were more likely to view AI disclosure as a sign of transparency, and some rated clearly labeled AI-generated content as more credible than unlabeled content. Transparency gives audiences the necessary context to evaluate what they are seeing, but it may not resolve every dispute about how images are made. Responsible use of AI-generated scientific images will require honesty, adherence to professional norms and the collective development ofevidence-based standardsacross fields. Why authentic images remain powerfulThe original Apollo 8 “Earthrise” photograph of 1968 carriessignificant emotional impact. So do the Artemis II images of 2026. What makes them meaningful is not simply their beauty. It is their traceable connection to scientific reality. When people look at these photographs of planets, they also know there are astronauts, physical cameras, documented missions and verifiable observations behind the images. In this sense,authenticity is a documented relationshipbetween an image and the world. In the age of generative AI, scientific institutions can no longer assume audiences will automatically trust their visuals. Trust now depends on transparency, documentation and clear communication about how visual evidence is produced. Without guidelines and standards, science risks entering a world where every image can be questioned and no image carries inherent credibility.