Methodology
EasyNIH is source-first. It is built to make papers easier to understand without cutting the connection back to the original PubMed or NIH record.
Contents
The core article workflow uses PubMed records and related NIH-linked source pages. EasyNIH keeps the original citation, PMID, PubMed link, and when available the full-text PubMed Central link attached to the breakdown.
Search ranking starts with live PubMed relevance and EasyNIH’s own article/search popularity signals. The article explanations are not treated as replacements for the source record.
EasyNIH uses AI to explain article abstracts in a more readable format. The system is instructed to stay grounded in the paper title and abstract, separate methods from findings, and define uncommon medical and research terms.
The site also caches article analyses so the same paper does not need a fresh AI call every time unless the paper data or EasyNIH’s review version changes.
Current-topics pages are separate from the article breakdown system. They are refreshed from saved snapshots and use a blend of EasyNIH search activity, NIH news matches, and recent PubMed publication activity.
They are meant to show what is moving in public health and medical-interest topics, not to serve as clinical guidance.
The site can be wrong. AI-assisted summaries can miss context, oversimplify, or explain a term poorly. Users can request edits or contact contact@easynih.org when something needs to be fixed.
EasyNIH treats the original linked source as the source of truth. When there is a conflict between the breakdown and the source record, the source record wins.