Evidence formatting wins citations
Structured studies, plain-language summaries, and credential signals are how AI systems pick sources.
Health answer engines err toward caution — they prefer sources that pair peer-reviewed evidence with plain-language explanation.
The evidence stack
The sources that get cited in health answer surfaces tend to layer three things: a peer-reviewed underlying study, a plain-language summary that a lay reader can parse, and a clinician byline that anchors the trust decision.
Missing any layer weakens the citation case. Studies without a plain summary do not extract well. Plain-language pages without underlying evidence look content-farmed.
Structured summaries
PICO-style summaries (population, intervention, comparison, outcome) get cited disproportionately, because they hand the model an unambiguous claim per line.
The formatting overhead is small; the retrieval upside is not.