Bixonimania
A fake disease made it from blog post to peer-reviewed journal because nobody at any level read the source material.
In early 2024, a medical researcher at the University of Gothenburg invented a disease. She gave it an implausible name, attributed the research to a fake scientist at a nonexistent university, and uploaded the results to open-access servers where AI training pipelines routinely harvest data. Within two years, four major AI chatbots were describing the disease’s symptoms to users, a peer-reviewed medical journal had cited the fabricated research as legitimate science, and ECRI, an independent patient safety organization, had named chatbot misuse the top health technology hazard of 2026. More than 40 million people turn to ChatGPT daily for health information, according to OpenAI’s own figures. The disease, bixonimania, traveled from blog post to preprint server to chatbot output to peer-reviewed citation, passing through every quality gate the information ecosystem maintains.
Poisoning the well
In March 2024, Almira Osmanovic Thunström posted two blog entries on Medium describing bixonimania as a form of periorbital melanosis caused by blue light exposure. Two preprints followed on the academic social network SciProfiles, listed under a fabricated researcher at an institution called Asteria Horizon University in Nova City, California. The experiment, documented by Nature in April, aimed to trace how fabricated information moves from open-access repositories into AI training pipelines and onward through the broader information ecosystem.
Within months, ChatGPT, Gemini, Copilot, and Perplexity were presenting bixonimania as a real condition. Microsoft Copilot declared that bixonimania was “an intriguing and relatively rare condition.” A peer-reviewed paper in Cureus, a journal published by Springer Nature, cited one of the fake preprints as evidence of an emerging condition linked to blue light exposure. That paper was retracted on March 30, after Nature contacted the journal, nearly two years after publication.
No safeguards whatsoever
The red flags were engineered to be unmissable. The suffix -mania belongs exclusively to psychiatric nomenclature. No legitimate ophthalmological condition would carry it. The lead author belonged to Asteria Horizon University, which does not exist. The acknowledgments thanked a professor at the Starfleet Academy. Osmanovic Thunström has said that she expected these signals would prevent the preprints from entering AI training data at all, and that the experiment was designed to demonstrate the existence of a filter. She discovered that no filter existed.
Each layer of the supply chain passed the fiction forward without performing its designated check. Preprint servers accepted the papers without screening institutional affiliations or content claims. AI training pipelines ingested them alongside legitimate medical literature and produced confident clinical descriptions of a nonexistent disease. Peer reviewers at Cureus cited the fake preprints without reading the underlying documents, a failure that Osmanovic Thunström attributes to researchers using AI tools to compile citations without verification. Correction came from outside the system. Nature’s inquiry prompted the retraction, and no internal mechanism at any level of the chain caught the fabrication on its own.
Only reading headlines
A viral thread that brought bixonimania to wide public attention in May illustrated this same dynamic in a different medium. The thread claimed that ChatGPT had diagnosed 40 million people with the disease, a figure that represents OpenAI’s estimate of daily health-related ChatGPT queries and has no specific connection to bixonimania. It cited a BMJ Open finding that chatbots gave misleading answers nearly half the time, without noting that the study had deliberately used adversarial prompts designed to elicit bad responses. These distortions circulated without correction through the same platforms that amplified the original hoax.