Shown a photograph of a red-capped scaber stalk (Leccinum aurantiacum) partially hidden by lingonberry shrubs, twelve AI-based fungi identification tools were asked what the mushroom was - and none of them named it. Copilot answered Boletus reticulatus. Champignouf answered Psathyrella candolleana. Yandex recognised only the lingonberry leaves. Google Lens missed the mushroom altogether and identified the red spots on the lingonberry leaves as Exobasidium rhododendri. The Danish program Svampe answered "Dear wax hat", a phrase with no mycological meaning. Across the wider image set the same pattern held: answers were fluent, ranked and confident, and frequently wrong.

Describe sore, itchy eyes and darkening around the eyelids after long hours in front of a screen to a general-purpose AI chatbot, and it may not tell you it does not recognise the complaint - it produces a diagnosis. Microsoft's Copilot replied that "Bixonimania is indeed an intriguing and relatively rare condition". Google's Gemini explained that "Bixonimania is a condition caused by excessive exposure to blue light". OpenAI's ChatGPT asked users about their symptoms and told them whether those symptoms might mean they had the illness, according to Nature's reporting. Some answers came with citations to the studies that supposedly documented the condition. Bixonimania does not exist. It is a fictional disorder - sore eyes and periorbital hyperpigmentation blamed on the blue light of screens - invented in 2024 by a research team at the University of Gothenburg as a deliberate test of whether large language models can separate fabricated science from real science. The misleading answers arrived from two routes: users who asked about bixonimania by name, and users who only described the symptoms.

Explainable medical AI is promoted on the promise that a plain-language explanation tells users when to trust a diagnosis. In a controlled skin-disease study, the large language model explanations did the opposite for the people who relied on them most: non-experts trusted LLM explanations whether they were right or wrong, were measurably more confident in their wrong answers when an LLM explanation was attached, and rated explanations as more convincing when they were vague or generic. Non-expert accuracy rose mainly because users deferred to the model, and that deference hurt more when the model was wrong than it helped when the model was right. Clinicians, by contrast, were not tripped up by incorrect AI explanations and did best with a bare prediction and no explanation at all.