The image of Earth glowing against the backdrop of space, captured during NASA’s Artemis II mission in April 2026, is a testament to human achievement. Yet, in an age where artificial intelligence can generate visually convincing images in seconds, how can the public discern the real from the fabricated?
This question is not merely about misinformation. As a researcher focusing on visual science communication and public trust I believe the proliferation of AI-generated science images is contributing to a broader crisis of trust in science. The tools scientists have long relied on to establish visual credibility are losing their effectiveness.
The Infiltration of AI-Generated Images in Science
AI tools are transforming how scientific visuals are created, shared, and publicized. Researchers use them to generate illustrations, create synthetic data, edit lab images, and produce educational materials. While these tools enhance creativity and efficiency, they also blur the lines between illustration, enhancement, and fabrication.
In 2026, two research papers were retracted after publishing AI-generated figures with biologically impossible structures. In April 2026, the New England Journal of Medicine retracted a paper due to an AI-manipulated clinical image. These incidents are likely just the tip of the iceberg, with researchers warning about the growing threats in fields like materials science that rely heavily on visual evidence.
The Challenge of Detecting AI-Generated Images
Academic publishers are beginning to adopt AI-detection tools. However, these systems often lag behind the technologies designed to create the images. Many detectors can only identify patterns they were trained to recognize, requiring constant updates as new AI models emerge. The biggest concern is realistic-looking visuals that subtly distort scientific details while remaining believable enough to pass initial review.
The Erosion of Trust in Scientific Images
For decades, scientific images carried authority because they were difficult 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 suggests that people judge scientific visuals using mental shortcuts. Does the image look technically sophisticated? Does it come from a trusted institution? Does it match what I already believe? Generative AI is undermining all three of these heuristics.
Today, anyone can create a polished, scientific-looking image from a text prompt. Images are often detached from their original source when circulating online. When visual quality and institutional attribution become unreliable cues, people tend to fall back on their own prior beliefs.
The Amplification of Motivated Reasoning
As a result, authentic scientific images that challenge someone’s existing beliefs can be dismissed as AI-generated, while fabricated images that confirm them are easily accepted as evidence. AI, in this way, may amplify motivated reasoning—the tendency to accept what one already agrees with and question what one does not.
This shift matters because visuals have long served as evidence for scientific claims. Nonexpert audiences rely on images not only to see what scientists have discovered but also to develop an emotional connection and perceive credibility in the science being presented. If audiences stop trusting visual evidence altogether, science loses one of its most powerful tools for public communication.
Transparency as the Path Forward
AI tools offer real benefits for researchers communicating their work to diverse audiences. The challenge is using these tools without transferring AI’s credibility deficit onto the science the images are meant to convey.
One practical path forward is for researchers to treat image provenance—where an image came from and how it was created—with the same seriousness they apply to data provenance. Scientists routinely disclose funding resources, study methodologies, and conflicts of interest. Similar standards may 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?
My colleagues and I found that people’s familiarity with AI significantly shapes how 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 of evidence-based standards across fields.
The Power of Authentic Images
The original Apollo 8 “Earthrise” photograph of 1968 carries significant 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 relationship between 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.
