In April 2026, astronauts aboard NASA's Artemis II mission captured a photograph of Earth glowing above the Moon's cratered horizon. The image spread widely online, evoking the emotional weight of the Apollo 8 "Earthrise." Yet its release also laid bare a accelerating problem: with AI tools able to fabricate a visually similar image from a text prompt in seconds, the public is losing the ability to distinguish authentic science visuals from synthetic ones. This erosion is more than a misinformation challenge-it is triggering a trust crisis in science itself, as the cues that once gave scientific images authority are losing their grip.
AI-generated images slip into scientific publishing
Researchers now routinely use AI to generate illustrations, create synthetic data, edit lab images, and produce outreach materials. The same tools that make complex ideas more accessible also blur the line between enhancement and fabrication. In 2024, two papers were retracted after publishing AI-generated figures that contained biologically impossible structures. In April 2026, the New England Journal of Medicine retracted a paper when it discovered that a clinical image had been manipulated with AI. These retractions, researchers say, are "likely just the tip of the iceberg," particularly in visual evidence-heavy fields such as materials science. Academic publishers are adopting detection tools, but detection systems almost always lag behind the models creating fakes. For professionals who want to understand these dynamics, collections of AI for Science & Research resources provide guidance on navigating AI's growing role in visual communication.
The crumbling of visual credibility cues
For decades, scientific images carried weight because they were difficult to produce. Microscope photographs, climate graphs, and space images demanded expensive equipment, institutional backing, and specialized expertise. Most audiences assumed such images represented true observations. Research shows that people judge scientific visuals using a few mental shortcuts: technical sophistication, a trusted institutional source, and alignment with what they already believe. Generative AI undercuts all three. Anyone can now create a polished, scientific-looking image from a prompt. Online, images become detached from their origins. When visual quality and institutional labels are no longer reliable indicators, people default to prior beliefs. Authentic images that challenge those beliefs can be dismissed as AI fakes, while fabricated ones that confirm them get accepted as evidence. This dynamic reinforces motivated reasoning-the urge to accept what fits preexisting views and reject what does not.
Transparency as the new standard
The challenge is not to reject AI tools but to use them without silently transferring AI's credibility deficit onto the science those images represent. One fix is to treat image provenance with the same rigor as data provenance. Scientists routinely disclose funding sources, methodologies, and conflicts of interest. Similar standards are now necessary for visuals. Was AI used to generate or modify this image? Is it a direct observation, a simulation, or an illustration? Research on AI disclosure found that people familiar with AI tools are more likely to view clearly labeled AI-generated content as credible. For research professionals, building this familiarity can start with structured training. An AI Learning Path for Research Scientists walks through integrating AI responsibly into scientific workflows and communicating that use clearly.
Why authentic images still hold power
The Apollo 8 "Earthrise" photograph from 1968 and the Artemis II images from 2026 are powerful beyond their aesthetic appeal. Their meaning comes from a documented connection to scientific reality-there are astronauts, physical cameras, documented missions, and verifiable observations behind them. Authenticity, in this sense, is a proven relationship between an image and the world. Scientific institutions can no longer assume that audiences will automatically trust their visuals. Trust now depends on clear documentation of how visual evidence is produced.
Why this matters for Science and Research
Researchers and science communicators face a new obligation. Disclosing AI use in image creation should become as routine as citing data sources. Labs and institutions need to establish guidelines for image provenance, and individual scientists should seek out training that demystifies AI tools. Without these standards, scientific visuals risk entering an environment where every image is questioned and none carries inherent credibility. The goal is not to resist AI, but to ensure that the public can continue to trust the visual evidence on which science depends.
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