How Supplement AI Evaluates Evidence

Supplement AI's evidence rating is a novel framework designed to help you and us determine how much weight to place on the outcomes and conclusions of a given research paper.

Evidence rating isn't meant to discredit studies; it's about consistency. Scientific literature is vast and often uneven in quality. By applying a uniform and transparent set of standards, Supplement AI makes it easier to identify which studies are most trustworthy and which should be interpreted with caution.

Our Research Database

Our research database includes over 185,000 papers and counting. Traditionally, studies are ranked by relevance or citation metrics alone. Supplement AI goes further by analyzing each paper's methodological rigor, transparency, and reliability along with factors like journal quality and citation influence.

Methodology

To evaluate methodology, we draw on established frameworks in evidence-based research, including principles from Cochrane, NIH/NHLBI, PRISMA, ARRIVE, and CARE guidelines. Each study type—clinical trial, observational study, systematic review, preclinical model, or case report—is assessed within its own methodological context. This ensures our scoring reflects how well a study was designed and executed, not simply what it found.

The 100-Point Scale

Scores are reported on a 100-point scale, where higher scores indicate stronger, more reliable research. A score near zero reflects serious methodological flaws, while a perfect score represents exceptional scientific rigor.

Dynamic and Transparent

Evidence rating is dynamic, evolving as our analytic models improve. Ratings are recalculated for greater precision but always using consistent, unbiased criteria. If a rating appears inaccurate, users can flag it for reassessment. We never manually change scores, but we continually refine how they're determined.

In short, evidence rating turns thousands of disconnected studies into a clear, trustworthy foundation for understanding supplements, bringing order and reliability to an often confusing field of research.